arXiv subjects

Amir G. Aghdam

Publications and source records attributed to Amir G. Aghdam.

At least 19 recordsLinked to original sources

arXiv2026

Underwater Barrier Sensing Under a Network False-Alarm Budget

We study joint sensor placement and detector operating-point allocation for guarding a region against an adversarial crossing. Each sensor may adjust its detection threshold, while all sensors draw on a shared network-level false-alarm budget. For a Gaussian signal-excess model of a square-law detector, we give a necessary and sufficient condition for the negative-log miss hazard to be concave in a sensor's false-alarm probability. At fixed deployment, this yields a concave max-min allocation whose saddle point equalizes marginal barrier gain per unit alarm against a worst-case occupation measure. A perspective reformulation supplies linear cuts for the mixed-integer joint problem, and a separate certificate lower-bounds the continuum barrier strength independently of the graph used for path search. On a deep-water case study based on the canonical Munk profile, reallocating a fixed alarm budget raises the graph design objective by 14.9% at unchanged sensor count and alarm load, and the certificate establishes the barrier requirement on the interpolated continuum field, which the graph value alone cannot.

eess.SY↗

arXiv2026

Explicit Sequential Equilibria in LQ Deep Structured Games and Weighted Mean-Field Games

We investigate a class of nonzero-sum dynamic stochastic games where players have linear dynamics and quadratic costs, coupled in both dynamics and cost through a linear regression (weighted average) and a quadratic regression (weighted covariance matrix) of states and actions; the linear regression of states is called the deep state. We study collaborative and non-collaborative games under three information structures: perfect sharing, deep state sharing, and no sharing, for three weight types: positive, homogeneous, and asymptotically vanishing. For perfect and deep state sharing structures, we propose a new technique using gauge transformation to solve the players' best-response equations and identify sufficient conditions for a unique subgame perfect Nash equilibrium. The equilibrium is linear in the local state and deep state, with gains obtained from a novel non-standard Riccati equation whose dimension is independent of the number of players, making the solution scalable. Under no sharing with an asymptotically large number of players, we propose an asymptotic population-size-dependent equilibrium and an asymptotic population-size-independent equilibrium (the sequential weighted mean-field equilibrium), and establish their convergence to the infinite-population limits. The main results are extended to infinite-horizon costs and generalized to multiple linear regressions and heterogeneous sub-populations. A numerical example illustrates the difference between the two approximate equilibria. To our knowledge, this is the first paper to propose a unified framework yielding the exact closed-form solution for an arbitrary number of players.

math.OC↗

arXiv2025

End-to-End Fine-Tuning of 3D Texture Generation using Differentiable Rewards

While recent 3D generative models can produce high-quality texture images, they often fail to capture human preferences or meet task-specific requirements. Moreover, a core challenge in the 3D texture generation domain is that most existing approaches rely on repeated calls to 2D text-to-image generative models, which lack an inherent understanding of the 3D structure of the input 3D mesh object. To alleviate these issues, we propose an end-to-end differentiable, reinforcement-learning-free framework that embeds human feedback, expressed as differentiable reward functions, directly into the 3D texture synthesis pipeline. By back-propagating preference signals through both geometric and appearance modules of the proposed framework, our method generates textures that respect the 3D geometry structure and align with desired criteria. To demonstrate its versatility, we introduce three novel geometry-aware reward functions, which offer a more controllable and interpretable pathway for creating high-quality 3D content from natural language. By conducting qualitative, quantitative, and user-preference evaluations against state-of-the-art methods, we demonstrate that our proposed strategy consistently outperforms existing approaches. Our implementation code is publicly available at: https://github.com/AHHHZ975/Differentiable-Texture-Learning

cs.CV↗

arXiv2025

A Distributed Gradient-Based Deployment Strategy for a Network of Sensors with a Probabilistic Sensing Model

This paper presents a distributed gradient-based deployment strategy to maximize coverage in hybrid wireless sensor networks (WSNs) with probabilistic sensing. Leveraging Voronoi partitioning, the overall coverage is reformulated as a sum of local contributions, enabling mobile sensors to optimize their positions using only local information. The strategy adopts the Elfes model to capture detection uncertainty and introduces a dynamic step size based on the gradient of the local coverage, ensuring movements adaptive to regional importance. Obstacle awareness is integrated via visibility constraints, projecting sensor positions to unobstructed paths. A threshold-based decision rule ensures movement occurs only for sufficiently large coverage gains, with convergence achieved when all sensors and their neighbors stop at a local maximum configuration. Simulations demonstrate improved coverage over static deployments, highlighting scalability and practicality for real-world applications.

eess.SY↗

arXiv2025

Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction

This paper presents a method for load balancing and dynamic pricing in electric vehicle (EV) charging networks, utilizing reinforcement learning (RL) to enhance network performance. The proposed framework integrates a pre-trained graph neural network to predict demand elasticity and inform pricing decisions. The spatio-temporal EV charging demand prediction (EVCDP) dataset from Shenzhen is utilized to capture the geographic and temporal characteristics of the charging stations. The RL model dynamically adjusts prices at individual stations based on occupancy, maximum station capacity, and demand forecasts, ensuring an equitable network load distribution while preventing station overloads. By leveraging spatially-aware demand predictions and a carefully designed reward function, the framework achieves efficient load balancing and adaptive pricing strategies that respond to localized demand and global network dynamics, ensuring improved network stability and user satisfaction. The efficacy of the approach is validated through simulations on the dataset, showing significant improvements in load balancing and reduced overload as the RL agent iteratively interacts with the environment and learns to dynamically adjust pricing strategies based on real-time demand patterns and station constraints. The findings highlight the potential of adaptive pricing and load-balancing strategies to address the complexities of EV infrastructure, paving the way for scalable and user-centric solutions.

eess.SY↗

arXiv2025

Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis

As the number of electric vehicles (EVs) continues to grow, the demand for charging stations is also increasing, leading to challenges such as long wait times and insufficient infrastructure. High-precision forecasting of EV charging demand is crucial for efficient station management, to address some of these challenges. This paper presents an approach to predict the charging demand at 15-minute intervals for the day ahead using a multivariate long short-term memory (LSTM) network with an attention mechanism. Additionally, the model leverages explainable AI techniques to evaluate the influence of various factors on the predictions, including weather conditions, day of the week, month, and any holiday. SHapley Additive exPlanations (SHAP) are used to quantify the contribution of each feature to the final forecast, providing deeper insights into how these factors affect prediction accuracy. As a result, the framework offers enhanced decision-making for infrastructure planning. The efficacy of the proposed method is demonstrated by simulations using the test data collected from the EV charging stations at California State University, Long Beach.

eess.SY↗

arXiv2025

Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning

This paper investigates unsupervised anomaly detection in multivariate time-series data using reinforcement learning (RL) in the latent space of an autoencoder. A significant challenge is the limited availability of anomalous data, often leading to misclassifying anomalies as normal events, thus raising false negatives. RL can help overcome this limitation by promoting exploration and balancing exploitation during training, effectively preventing overfitting. Wavelet analysis is also utilized to enhance anomaly detection, enabling time-series data decomposition into both time and frequency domains. This approach captures anomalies at multiple resolutions, with wavelet coefficients extracted to detect both sudden and subtle shifts in the data, thereby refining the anomaly detection process. We calibrate the decision boundary by generating synthetic anomalies and embedding a supervised framework within the model. This supervised element aids the unsupervised learning process by fine-tuning the decision boundary and increasing the model's capacity to distinguish between normal and anomalous patterns effectively.

cs.LG↗

arXiv2025

Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder

Reliable aero-engine anomaly detection is crucial for ensuring aircraft safety and operational efficiency. This research explores the application of the Fisher autoencoder as an unsupervised deep learning method for detecting anomalies in aero-engine multivariate sensor data, using a Gaussian mixture as the prior distribution of the latent space. The proposed method aims to minimize the Fisher divergence between the true and the modeled data distribution in order to train an autoencoder that can capture the normal patterns of aero-engine behavior. The Fisher divergence is robust to model uncertainty, meaning it can handle noisy or incomplete data. The Fisher autoencoder also has well-defined latent space regions, which makes it more generalizable and regularized for various types of aero-engines as well as facilitates diagnostic purposes. The proposed approach improves the accuracy of anomaly detection and reduces false alarms. Simulations using the CMAPSS dataset demonstrate the model's efficacy in achieving timely anomaly detection, even in the case of an unbalanced dataset.

eess.SP↗

arXiv2024

Temporally Consistent Object Editing in Videos using Extended Attention

Image generation and editing have seen a great deal of advancements with the rise of large-scale diffusion models that allow user control of different modalities such as text, mask, depth maps, etc. However, controlled editing of videos still lags behind. Prior work in this area has focused on using 2D diffusion models to globally change the style of an existing video. On the other hand, in many practical applications, editing localized parts of the video is critical. In this work, we propose a method to edit videos using a pre-trained inpainting image diffusion model. We systematically redesign the forward path of the model by replacing the self-attention modules with an extended version of attention modules that creates frame-level dependencies. In this way, we ensure that the edited information will be consistent across all the video frames no matter what the shape and position of the masked area is. We qualitatively compare our results with state-of-the-art in terms of accuracy on several video editing tasks like object retargeting, object replacement, and object removal tasks. Simulations demonstrate the superior performance of the proposed strategy.

cs.CV↗

arXiv2023

Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism

Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. The method is used to estimate appliance-level power consumption from aggregated power measurements. This paper presents a hybrid learning approach, consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory (BILSTM), featuring an integrated attention mechanism, all within the context of disaggregating low-frequency power data. While prior research has been mainly focused on high-frequency data disaggregation, our study takes a distinct direction by concentrating on low-frequency data. The proposed hybrid CNN-BILSTM model is adept at extracting both temporal (time-related) and spatial (location-related) features, allowing it to precisely identify energy consumption patterns at the appliance level. This accuracy is further enhanced by the attention mechanism, which aids the model in pinpointing crucial parts of the data for more precise event detection and load disaggregation. We conduct simulations using the existing low-frequency REDD dataset to assess our model performance. The results demonstrate that our proposed approach outperforms existing methods in terms of accuracy and computation time.

eess.SY↗

arXiv2023

A Distributed Strategy to Maximize Coverage in a Heterogeneous Sensor Network in the Presence of Obstacles

In this paper, an efficient deployment strategy is proposed for a network of mobile and static sensors with nonidentical sensing and communication radii. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is used to partition the field and assign the underlying coverage task to each mobile sensor. A gradient-based method is applied to find the best candidate point based on the detected coverage holes and the coverage priority considering the relative distance of the mobile sensor from the static ones and the obstacles in the field. The sensors move to a new position if such a relocation increases their local coverage. The efficiency of the proposed strategy in different scenarios is demonstrated by simulations.

eess.SY↗

arXiv2023

Generalized Power Iteration with Application to Distributed Connectivity Estimation of Asymmetric Networks

The problem of connectivity assessment in an asymmetric network represented by a weighted directed graph is investigated in this article. A power iteration algorithm in a centralized implementation is developed first to compute the generalized algebraic connectivity of asymmetric networks. After properly transforming the Laplacian matrix of the network, two sequences of one-dimensional and two-dimensional subspaces are generated iteratively, one of which converges to the desired subspace spanned by the eigenvector(s) associated with the eigenvalue(s) representing the network's generalized algebraic connectivity. A distributed implementation of the proposed power iteration algorithm is then developed to compute the generalized algebraic connectivity from the viewpoint of each node, which is scalable to any asymmetric network of any size with a fixed message length per node. The convergence analysis of these algorithms is subsequently provided under some weak assumptions. The efficiency of the developed algorithms in computing the network connectivity is then demonstrated by simulations.

eess.SY↗

arXiv2023

Risk-Constrained Control of Mean-Field Linear Quadratic Systems

The risk-neutral LQR controller is optimal for stochastic linear dynamical systems. However, the classical optimal controller performs inefficiently in the presence of low-probability yet statistically significant (risky) events. The present research focuses on infinite-horizon risk-constrained linear quadratic regulators in a mean-field setting. We address the risk constraint by bounding the cumulative one-stage variance of the state penalty of all players. It is shown that the optimal controller is affine in the state of each player with an additive term that controls the risk constraint. In addition, we propose a solution independent of the number of players. Finally, simulations are presented to verify the theoretical findings.

eess.SY↗

arXiv2023

Early Malware Detection and Next-Action Prediction

In this paper, we propose a framework for early-stage malware detection and mitigation by leveraging natural language processing (NLP) techniques and machine learning algorithms. Our primary contribution is presenting an approach for predicting the upcoming actions of malware by treating application programming interface (API) call sequences as natural language inputs and employing text classification methods, specifically a Bi-LSTM neural network, to predict the next API call. This enables proactive threat identification and mitigation, demonstrating the effectiveness of applying NLP principles to API call sequences. The Bi-LSTM model is evaluated using two datasets. %The model achieved an accuracy of 93.6\% and 88.8\% for the %first and second dataset respectively. Additionally, by modeling consecutive API calls as 2-gram and 3-gram strings, we extract new features to be further processed using a Bagging-XGBoost algorithm, effectively predicting malware presence at its early stages. The accuracy of the proposed framework is evaluated by simulations.

cs.CR↗

arXiv2022

Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision

A key question in the problem of 3D reconstruction is how to train a machine or a robot to model 3D objects. Many tasks like navigation in real-time systems such as autonomous vehicles directly depend on this problem. These systems usually have limited computational power. Despite considerable progress in 3D reconstruction systems in recent years, applying them to real-time systems such as navigation systems in autonomous vehicles is still challenging due to the high complexity and computational demand of the existing methods. This study addresses current problems in reconstructing objects displayed in a single-view image in a faster (real-time) fashion. To this end, a simple yet powerful deep neural framework is developed. The proposed framework consists of two components: the feature extractor module and the 3D generator module. We use point cloud representation for the output of our reconstruction module. The ShapeNet dataset is utilized to compare the method with the existing results in terms of computation time and accuracy. Simulations demonstrate the superior performance of the proposed method. Index Terms-Real-time 3D reconstruction, single-view reconstruction, supervised learning, deep neural network

cs.CV↗

arXiv2022

A Versatile Pseudo-Rigid Body Modeling Method

A novel semi-analytical method is proposed to develop the pseudo-rigid-body~(PRB) model of robots made of highly flexible members (HFM), such as flexures and continuum robots, with no limit on the degrees of freedom of the PRB model. The proposed method has a simple formulation yet high precision. Furthermore, it can describe HFMs with variable curvature and stiffness along their length. The method offers a semi-analytical solution for the highly coupled nonlinear constrained optimization problem of PRB modeling and can be extended to variable-length robots comprised of HFM, such as catheter and concentric tube robots. We also show that this method can obtain a PRB model of uniformly stiff HFMs, with only three parameters. The versatility of the method is investigated in various applications of HFM in continuum robots. Simulations demonstrate substantial improvement in the precision of the PRB model in general and a reduction in the complexity of the formulation.

cs.RO↗

arXiv2022

On Forward Kinematics of a 3SPR Parallel Manipulator

In this paper, a new numerical method to solve the forward kinematics (FK) of a parallel manipulator with three-limb spherical-prismatic-revolute (3SPR) structure is presented. Unlike the existing numerical approaches that rely on computation of the manipulator's Jacobian matrix and its inverse at each iteration, the proposed algorithm requires much less computations to estimate the FK parameters. A cost function is introduced that measures the difference of the estimates from the actual FK values. At each iteration, the problem is decomposed into two steps. First, the estimates of the platform orientation from the heave estimates are obtained. Then, heave estimates are updated by moving in the gradient direction of the proposed cost function. To validate the performance of the proposed algorithm, it is compared against a Jacobian-based (JB) approach for a 3SPR parallel manipulator.

cs.RO↗

arXiv2021

Deep Structured Teams in Arbitrary-Size Linear Networks: Decentralized Estimation, Optimal Control and Separation Principle

In this article, we introduce decentralized Kalman filters for linear quadratic deep structured teams. The agents in deep structured teams are coupled in dynamics, costs and measurements through a set of linear regressions of the states and actions (also called deep states and deep actions). The information structure is decentralized, where every agent observes a noisy measurement of its local state and the global deep state. Since the number of agents is often very large in deep structured teams, any naive approach to finding an optimal Kalman filter suffers from the curse of dimensionality. Moreover, due to the decentralized nature of information structure, the resultant optimization problem is non-convex, in general, where non-linear strategies can outperform linear ones. However, we prove that the optimal strategy is linear in the local state estimate as well as the deep state estimate and can be efficiently computed by two scale-free Riccati equations and Kalman filters. We propose a bi-level orthogonal approach across both space and time levels based on a gauge transformation technique to achieve the above result. We also establish a separation principle between optimal control and optimal estimation. Furthermore, we show that as the number of agents goes to infinity, the Kalman gain associated with the deep state estimate converges to zero at a rate inversely proportional to the number of agents. This leads to a fully decentralized approximate strategy where every agent predicts the deep state by its conditional and unconditional expected value, also known as the certainty equivalence approximation and (weighted) mean-field approximation, respectively.

cs.MA↗