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

This article proposes an integrated fault-tolerant control architecture for unidirectional constrained control of elastic cable-driven parallel robots (CDPRs). In CDPRs, the cable tension can only be positive, requiring unidirectional control effort. In the proposed structure, the positive tension distribution (PTD) of the elastic CDPR is ensured using a prevention function. Compared to existing methods for elastic CDPRs, this new technique has significantly lower computation time, which is essential for real-time applications. Moreover, two adaptive observers are utilized to tackle undesirable events, particularly partial failures. The stability analysis of the closed-loop system is provided using Lyapunov’s second method. The simulation study shows that the proposed method improves the tracking accuracy and is significantly faster than conventional methods. The effectiveness of the proposed method is evaluated in the presence of model uncertainties and various faulty conditions.

A new distributed adaptive fault-tolerant tracking controller is proposed for consensus control of high-order nonlinear multi-agent systems in the presence of actuator faults and unknown time-varying delays. The system is subject to uncertain disturbances under directed topology. The proposed adaptive method is based on dynamic surface control, where the nonlinear dynamics are unknown, and are approximated by a radial basis function neural network. This system model is subject to delay, fault, uncertainty, disturbances, and singularity of the input signal. The typical constraints on time-delay terms, addressed in the related literature, are relaxed. This relaxation, however, can lead to singularity in the controller. This drawback is addressed by using a specific differentiable function in the control law. It is shown that the generated closed-loop signals are uniformly semi-globally bounded, and the tracking errors converge to a neighborhood of the origin. The effectiveness of the proposed controller is verified by simulations. Note to Practitioners—This work focuses on the distributed control of nonlinear multi-agent time-delay systems in the presence of actuator faults. The proposed control design has potential applications in various real-world network systems such as smart grids, transportation networks, autonomous vehicles, etc. Note that many practical multi-agent systems are subject to time delay and parameter uncertainties, and ignoring these in the control design can negatively impact the system performance and even destabilize it. We demonstrate that the resulting closed-loop system is uniformly semi-globally stable and that the tracking error converges to a neighborhood of the origin. The boundedness assumption on time delay terms, often considered in the multi-agent system literature, is relaxed here. Consequently, the obtained results closely align with practical situations. In addition, the explosion of complexity problem has been avoided by using the dynamic surface control method. The dynamic system represents an unknown-structure dynamic model. This type of dynamic model is applicable to various practical engineering systems. Particularly, robotic systems, chemical reactor systems and spring-mass-damper systems can be described by a model similar to this dynamic system.

In cable-driven parallel robots (CDPRs), the cable tension must be in an admissible positive range. The positive tension distribution (PTD) in CDPR is generally guaranteed using optimization-based methods, limiting their real-time applications due to the unpredictable worst-case computation time of such methods. In this study, we consider the PTD to be an integral part of the control system and introduce a computationally efficient control-based approach for generating positive tensions. This innovative approach offers several benefits for controlling redundant CDPRs. It significantly improves computation time compared to existing iterative methods, eliminates high sensitivity to kinematic uncertainties, and enhances trajectory tracking performance. Additionally, the proposed method keeps the robot on the boundary of the workspace when the trajectory exits the wrench-feasible workspace. To this end, we introduce a novel representation of CDPR dynamics as a nonaffine form, considering the tension of the cables as state variables. A robust unilateral constrained control structure is proposed using a third-order model, ensuring control efforts remain within a prescribed positive range. The computational efficiency of the method is investigated in a hyper-redundant CDPR and is compared with the conventional methods. It is also shown that for the desired trajectory out of the workspace, the proposed method keeps the robot at the boundary of the workspace while maintaining positive tensions.

Energy harvesting (EH) is the process of capturing and storing energy from external sources or the ambient environment. The EH devices have found various emerging applications, particularly, in healthcare sector. Kinetic-based micro energy-harvesting is a promising technology that could prolong the lifetime of batteries in small wearable or implantable devices. In this paper, using a mathematical model of a Coulomb-force parametric generator, we analyze the dependency of the output power on the electrostatic force in this micro-harvester. We propose a very low complexity strategy to adaptively change the electrostatic force in order to maximize the harvested power. Simulation results using the human acceleration measurements confirm the effectiveness of the proposed strategy.

Miniaturized wearable or implantable medical sensors (or actuators) are often used in the Internet of Things (IoT) technologies in healthcare applications. However, their limited source of power is becoming a bottleneck for the pervasive use of these devices, especially, as their functionality increases. Kinetic-based micro-energy harvesters can generate power through the natural human body motion. Therefore, they can be an attractive solution to supplement the source of power in medical wearables or implants. The architecture based on the Coulomb force parametric generator (CFPG) is the most viable micro-harvester solution for generating power from human motion. This article proposes three methods: a linear estimation approach, a multi-armed bandit algorithm, and a min–max-based approach to adaptively estimate the desirable electrostatic force in a CFPG using the input acceleration waveform. Through extensive simulations, the performance of the proposed methods in maximizing the output power of the micro-harvester is evaluated.

In this work, efficient algorithms are developed to increase the area covered by a network of mobile sensors. The sensors are divided into$k$sets, and then the proposed algorithms perform iteratively to increase the area covered by at least$k$sensors as much as possible. Since the performance of the algorithms highly depends on the initial positions of sensors, we use the$K$-means clustering technique for partitioning the sensors into$k$sets. Simulation results confirm the effectiveness of the proposed algorithms. They also show that using the$K$-means clustering technique improves the performance of the algorithms in terms of energy consumption, covered area, and convergence time.

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.

This paper investigates the expected rate of convergence to consensus in an asymmetric network represented by a weighted directed graph. The initial state of the network is represented by a random vector and the expectation is taken with respect to the random initial condition of the network. The proposed convergence rate is described in terms of the eigenvalues of the Laplacian matrix of the network graph. The generalized power iteration algorithm is then introduced based on the Krylov subspace method to compute the proposed expected convergence rate in a centralized fashion. To this end, the Laplacian matrix of the network is transformed to a new matrix such that existing techniques can be used to find the eigenvalue representing the expected convergence rate of the network. The convergence analysis of the centralized algorithm is performed with a prescribed upper bound on the approximation error of the algorithm. A distributed version of the centralized algorithm is then developed using the notion of consensus observer. The efficiency of the algorithms is subsequently demonstrated by simulations.

In this paper, an energy-efficient strategy is proposed for tracking a moving target in an environment with obstacles, using a network of mobile sensors. Typically, the most dominant sources of energy consumption in a mobile sensor network are sensing, communication, and movement. The proposed algorithm first divides the field into a grid of sufficiently small cells. The grid is then represented by a graph whose edges are properly weighted to reflect the energy consumption of sensors. The proposed technique searches for near-optimal locations for the sensors in different time instants to route information from the target to destination, using a shortest path algorithm. Simulations confirm the efficacy of the proposed algorithm.

Mobile sensor networking technology has attracted considerable attention in various research communities in recent years due to their widespread applications in civilian and military environments. One objective when using mobile sensors is to obtain maximum field coverage by properly deploying sensor nodes. In many real-world applications a priori knowledge about the best deployment position for the sensors is not available. However, the motion capability of the sensors could allow each node to adjust its position (i.e. relocate) so that a better (and ultimately maximal) coverage is achieved. In this paper, a novel autonomous joint sensing range and relocation control algorithm is presented that achieves improved coverage and network lifetime at the same time. In the proposed algorithm, the sensing range of each sensor is adjusted iteratively based on its residual energy. At the same time, the sensor is directed to move within its corresponding multiplicatively weighted Voronoi (MW-Voronoi) region to ultimately increase sensing coverage in the field. Simulation results demonstrate the efficacy of the technique.

This paper investigates the global-connectivity assessment of a sensor network subject to random communications. The investigation exploits the corresponding expected communication graph and its associated weighted vertex connectivity (WVC). Computing the WVC measure for such networks is an NP-hard problem. This situation led to the development of an approximate WVC (AWVC) measure, which has its own shortcomings. This paper introduces an improved approximate weighted vertex connectivity (IAWVC) measure with a polynomial- time implementation. The new connectivity measure does not have the shortcomings of the AWVC measure and, under some conditions, matches the WVC measure. Simulation results show the efficiency of the IAWVC computation and its effectiveness in approximating the WVC measure.

This paper tackles the problem of minimum-energy cooperative transmission in wireless networks under the assumption of limited channel state information at the transmitters. It is assumed that only the average statistics of the fading channels are known to the transmitters. The objective here is to jointly optimize the set of relays and the transmission powers for both the broadcasting and cooperative transmission phases while satisfying probabilistic signal-to-noise ratio (SNR) constraints at the relays and at the destination node. Increasing the broadcasting power expands the set of potential relays and decreases the required power for cooperative transmission. Hence, there is a compromise in the selection of the power values, which is addressed in this work using a chance-constrained optimization framework. A closed-form approximate solution is also presented, which provides a low-complexity transmission scheme for energy-harvesting wireless networks. Simulations are presented to demonstrate the efficacy of the proposed approach and the approximate solution.

This paper is concerned with energy-efficient routing in wireless sensor networks. Most of the existing routing schemes assign energy-related costs to network links and obtain the shortest paths for the nodes to balance the flowing traffic within the network and increase its lifetime. However, the optimal link cost values and the maximum achievable lifetime are not known for the majority of the existing schemes. A framework is provided in this work to analytically derive the best achievable performance that can be obtained by any distributed routing algorithm based on the shortest-path approach. Given a network configuration and an energy consumption model, the presented framework provides the optimal link cost assignment which yields the maximum lifetime in a distributed shortest-path routing strategy. The results are extended to the case of variable link cost assignment as well. A heuristic algorithm is also developed to obtain approximate solutions to the best performance problem with limited computational complexity. In particular, the proposed framework provides the optimal route selection as a benchmark to evaluate the energy efficiency of existing routing algorithms.

In this paper, efficient sensor deployment strategies are developed to increase coverage in wireless mobile sensor networks. The sensors find coverage holes within their Voronoi polygons and then move in an appropriate direction to minimize them. Novel edge-based and vertex-based strategies are introduced, and their performances are compared with existing techniques. The proposed movement strategies are based on the distances of each sensor and the points inside its Voronoi polygon from the edges or vertices of the polygon. Simulations confirm the effectiveness of the proposed deployment algorithms and their superiority to the techniques reported in the literature.

An energy-efficient sensor deployment algorithm is developed in this paper for improving coverage in a network of mobile sensors with nonidentical sensing ranges. The proposed strategy iteratively computes the desirable position of each sensor in the field based on existing coverage holes. These holes are obtained using an additively weighted Voronoi (AW- Voronoi) diagram. The sensors move in their AW- Voronoi regions such that the coverage holes in the network become as small as possible. Simulation results demonstrate the efficacy of the proposed strategy and its superiority to existing algorithms which utilize multiplicatively weighted Voronoi and Voronoi-Laguree diagrams.

This paper presents an optimization framework for a wireless sensor network whereby, in a given route, the optimal relay selection and power allocation are performed subject to signal-to-noise ratio constraints. The proposed approach determines whether a direct transmission is preferred for a given configuration of nodes, or a cooperative transmission. In the latter case, for each node, data transmission to the destination node is performed in two consecutive phases: broadcasting and relaying. The proposed strategy provides the best set of relays, the optimal broadcasting power and the optimal power values for the cooperative transmission phase. Once the minimum-energy transmission policy is obtained, the optimal routes from every node to a sink node are built-up using cooperative transmission blocks. We also present a low-complexity implementation approach of the proposed framework and provide an explicit solution to the optimization problem at hand by invoking the theory of multi-parametric programming. This technique provides the optimal solution as a function of measurable parameters in an off-line manner, and hence the on-line computational tasks are reduced to finding the parameters and evaluating simple functions. The proposed efficient approach has many potential applications in real-world problems and, to the best of the authors' knowledge, it has not been applied to communication problems before. Simulations are presented to demonstrate the efficacy of the approach.

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a given function. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is utilized to find the coverage holes of the network for the case where the sensing ranges of different sensors are not the same. Under the proposed strategies, each sensor detects coverage holes within its MW-Voronoi region, and then moves in a proper direction to reduce their size. Since the coverage priority of the field is not uniform, the target location of each sensor is determined based on the weights of the vertices or the points inside the corresponding MW-Voronoi region. Simulations validate the theoretical results.

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a prescribed weight function. According to these strategies, each sensor detects coverage holes within its Voronoi polygon, and then moves in a proper direction to reduce them. Since the coverage priority of different points in the field is not the same, the target location of each sensor is determined based on the weights of the points inside the corresponding Voronoi polygon (or its vertices). Simulations confirm the theoretical results.

This paper reports on work in progress on developing a unified co-evolutionary/pricing framework for Mobile Sensor Networks (MSN) self-organization. MSN self-organization involves cooperative sensor positioning and formation of a multi-hop Mobile Ad-hoc NETwork (MANET) enabling sensor information acquisition and communication while prolonging MSN life-span. A number of inherent MSN traits such as lack of centralized control and variety of performance criteria suggest framing MSN self-organization as a sensor co-evolutionary optimization. The first issue to be addressed is aligning individual sensor utility/fitness landscapes with the overall MSN goals as “selfish” sensor behavior may result in suboptimal overall MSN performance. The paper proposes using “socially optimal pricing” to internalize the effect of each sensor relocation on the overall MSN performance. It is assumed that intermediate nodes in the MSN relay sensor information to a single fusion point without processing. The proposed framework is illustrated for the special case of a MSN tracking a single target.

In this paper, efficient sensor deployment algorithms are proposed to improve the coverage area in the target field. The proposed algorithms calculate the position of the sensors iteratively, based on the existing coverage holes in the target field. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is used to discover the coverage holes corresponding to different sensors with different sensing ranges. Under the proposed procedures, the sensors move in such a way that the coverage holes in the target field are reduced. Simulation results are provided to demonstrate the effectiveness of the deployment schemes proposed in this paper.

In this paper, the problem of resource allocation in IS-856 uplink in the presence of time-delay is studied. A set of nonlinear adaptive controllers are designed to stabilize the wireless network and use the system resources efficiently. The controllers obtained are then modified properly to retain network stability and performance in the presence of time-delay. Simulation results are presented to show the effectiveness of the proposed approach.

In this paper, data-rate assignment in IS-856 uplink (reverse link) is studied. The problem is first formulated in an interference model framework, and then a dynamic control strategy is developed for efficient rate assignment. In the first step, the controller is designed for the special case when the number of users in the network is fixed. Then, the minimum time required for a dynamic network (where the number of users is subject to change) to achieve a desired performance is obtained. The simulation results are presented to elucidate the effectiveness of the proposed approach.