Search Results (54)

The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical aspects of patient security. In this paper, we introduce a machine learning-enabled Cognitive Cyber-Physical System (ML-CCPS), which is designed to identify and respond to cyber threats in MIoT environments through a layered cognitive architecture. The system is constructed on a feedback-looped architecture integrating hybrid feature modeling, physical behavioral analysis, and Extreme Learning Machine (ELM)-based classification to provide adaptive access control, continuous monitoring, and reliable intrusion detection. ML-CCPS is capable of outperforming benchmark classifiers with an acceptable computational cost, as evidenced by its macro F1-score of 97.8% and an AUC of 99.1% when evaluated with the ToN-IoT dataset. Alongside classification accuracy, the framework has demonstrated reliable behaviour under noisy telemetry, maintained strong efficiency in resource-constrained settings, and scaled effectively with larger numbers of connected devices. Comparative evaluations, radar-style synthesis, and ablation studies further validate its effectiveness in real-time MIoT environments and its ability to detect novel attack types with high reliability. Full article

This research proposes a fractional-order adaptive neural control scheme using an optimized backstepping (OB) approach to address strict-feedback nonlinear systems with uncertain control directions and predefined performance requirements. The OB framework integrates both fractional-order virtual and actual controllers to achieve global optimization, while a Nussbaum-type function is introduced to handle unknown control paths. To ensure convergence to desired accuracy within a prescribed time, a fractional-order dynamic-switching mechanism and a quartic-barrier Lyapunov function are employed. An input-to-state practically stable (ISpS) auxiliary signal is designed to mitigate unmodeled dynamics, leveraging classical lemmas adapted to fractional-order systems. The study further investigates a decentralized control scenario for large-scale stochastic nonlinear systems with uncertain dynamics, undefined control directions, and unmeasurable states. Fuzzy logic systems are employed to approximate unknown nonlinearities, while a fuzzy-phase observer is designed to estimate inaccessible states. The use of Nussbaum-type functions in decentralized architectures addresses uncertainties in control directions. A key novelty of this work lies in the combination of fractional-order adaptive control, fuzzy logic estimation, and Nussbaum-based decentralized backstepping to guarantee that all closed-loop signals remain bounded in probability. The proposed method ensures that system outputs converge to a small neighborhood around the origin, even under stochastic disturbances. The simulation results confirm the effectiveness and robustness of the proposed control strategy. Full article

Nonlinear optimization with equation constraints has wide applications in intelligent control systems, acoustic signal processing, etc. Thus, effectively tackling the nonlinear optimization problems with equation constraints is of great significance for the advancement of these fields. Current discrete-time neurodynamics predominantly addresses unperturbed optimization scenarios, exhibiting inherent sensitivity to external noise, which limits the practical application of these methods. To address this issue, we propose a discrete-time noise-suppressed neurodynamics (DTNSN) model in this paper. First, the model integrates the static optimization stability of the gradient-based neurodynamics (GND) model with the time-varying tracking capability of the zeroing neurodynamics (ZND) model. Then, an integral feedback term is introduced to suppress external noise disturbances, thereby enhancing the robustness of the model. Additionally, to facilitate implementation on digital hardware, we employ an explicit linear three-step discretization method to obtain the proposed DTNSN model. Finally, the convergence performance, noise suppression capability, and practicality of the model are validated through theoretical analysis, numerical simulations, and acoustic source localization experiments. The model is applicable to the fields of intelligent control systems, acoustic signal processing, and industrial automation, providing new tools for real-time optimization in noisy environments. Full article

Future space missions will include multi-robot systems, with greater autonomy and a large degree of heterogeneity for a wider range of task capabilities and redundancy. It is imperative that both software (learning models, parallelizing capabilities, resource distribution, etc.) and hardware factors must be considered during decentralized task negotiation to lead to better performance of the team. By utilizing the formalism of contextual Markov decision processes, team composition can be incorporated into the learning process and used for more meaningful and reliable evaluation using measures such as total time, overall consumed energy, performance feedback from tasks, or damage incurred. Improved team performance will in turn enhance the overall results of the mission. Planetary exploration tasks often involve time, communication and energy constraints. Such missions are also prone to noisy sensor data (e.g., camera images distorted by dust), as well as wear and tear on hardware (e.g., wheels, manipulators). To ensure that such factors do not jeopardize the mission, they must be taken into account. Therefore, this paper describes a software framework for the reliable execution of tasks in constrained and dynamic environments. Our work leverages the advantages of heterogeneity for more resilient planetary missions by addressing two aspects—first, the integration of hardware parameters into the negotiation process, and second the analysis of how the integration of team performance metrics, particularly adaptability and mutual support, in task negotiation plays a role in the overall mission success. Full article

The adoption of kiosks in public spaces is steadily increasing, with a trend toward providing more natural user experiences through embodied conversational agents (ECAs). To achieve human-like interactions, ECAs should be able to appropriately gaze at the speaker. However, kiosks in public spaces often face challenges, such as ambient noise and overlapping speech from multiple people, making it difficult to accurately identify the speaker and direct the ECA’s gaze accordingly. In this paper, we propose a lightweight gaze control system that is designed to operate effectively within the resource constraints of kiosks and the noisy conditions common in public spaces. We first developed a speaker detection model that identifies the active speaker in challenging noise conditions using only a single camera and microphone. The proposed model achieved a 91.6% mean Average Precision (mAP) in active speaker detection and a 0.6% improvement over the state-of-the-art lightweight model (Light ASD) (as evaluated on the noise-augmented AVA-Speaker Detection dataset), while maintaining real-time performance. Building on this, we developed a gaze control system for ECAs that detects the dominant speaker in a group and directs the ECA’s gaze toward them using an algorithm inspired by real human turn-taking behavior. To evaluate the system’s performance, we conducted a user study with 30 participants, comparing the system to a baseline condition (i.e., a fixed forward gaze) and a human-controlled gaze. The results showed statistically significant improvements in social/co-presence and gaze naturalness compared to the baseline, with no significant difference between the system and human-controlled gazes. This suggests that our system achieves a level of social presence and gaze naturalness comparable to a human-controlled gaze. The participants’ feedback, which indicated no clear distinction between human- and model-controlled conditions, further supports the effectiveness of our approach. Full article

The present paper proposes a new Maximum Torque Per Ampere (MTPA) algorithm applied to sensorless speed control for the Synchronous Reluctance Machine (SynRM). The SynRM mathematical model is suitably modified and expressed in the γδ estimated reference frame, which could be applied in sensorless implementations. In the controller–observer scheme, an MTPA controller is coupled with a sliding mode observer (SMO) of first order. The provided equivalent control inputs are directly utilized by a modified EMF observer to estimate the rotor speed and position. Also, the MTPA control, SMO, and modified EMF observer are accordingly expressed in the γδ reference frame. In the duration of the SynRM operation, the developed MTPA algorithm succeeds in adjusting both stator current components in the γ-axis and δ-axis to the maximum torque point, while the SMO converges rapidly, achieving the coincidence between the γδ and dq reference frames. In addition, a simple torque decoupling technique is used to determine the γ-axis and δ-axis reference currents connected with the Anti-Windup Controller (AWC) for stator current control. Despite conventional MTPA methods, the proposed MTPA control strategy is designed to be robust in a wide speed range, exhibiting a high dynamic performance, regardless of the presence of external torque disturbances, reference speed variation, and even current measurement noise. The performance of the overall observer–control system is examined and evaluated using MATLAB/Simulink and considering noisy current feedback. Simulation results demonstrate the robustness and effectiveness of the proposed MTPA-based control method. Full article

Vehicle longitudinal trajectory tracking plays a significant role in developing ecorouting and autonomous driving systems to handle various disturbances and uncertainties (e.g., road grade, gust wind, etc.) that are often ignored by the optimization strategies used to generate reference controls and trajectories. In this paper, based on a linearized vehicle model with the help of feedback linearization, a linear quadratic integral tracking (LQIT) control is utilized to generate regulation laws to minimize the tracking error of optimal speed or brake distance trajectories, respectively, and maintain brake safety. A unified Kalman filter is used to estimate system states based on noisy measurements. Both acceleration and deceleration LQIT controls are designed to handle the change of upperlevel optimal control strategies to varying traffic. Simulation and co-simulation studies validated the proposed LQIT control strategies in Simulink with the SUMO traffic model using a real-world map under manipulated driving conditions. The simulation results show that under changing traffic conditions, the LQIT acceleration control is able to reduce the static tracking error by 99.8%, compared with the vehicle controlled only by the high-level optimal acceleration control without a trajectory tracker, achieving less tracking error and overshoot than using a PI control. The LQIT deceleration control reduces the brake distance error by 48% over the optimal deceleration control alone and ensures a safer brake distance than a coupled PI control. The traffic model used in the SUMO co-simulation confirms the capability of handling varying traffic for the developed LQIT control strategies. Full article

The development of resource-efficient digital technologies is a critical challenge in the wastewater sector. This industrial case study, conducted in collaboration with the Veas Water Resource Recovery Facility in Norway, focused on creating data pre-processing methods and resource-efficient control strategies. Using data from the Veas biogas plant, dynamic models were developed to compare control outcomes. The primary objective was to maximize biogas production and hot water usage while maintaining optimal temperature and hydraulic retention time by adjusting inlet sludge and hot water flow rates. Sequential operations were approximated as continuous operations using a 30-min moving minimum/maximum for bimodal data and a 2-h moving average for noisy data. The data-driven dynamic models achieved an accuracy of up to $R^2$ of 0.85. The control strategy, which included one feedback controller, one ratio controller, and flow rate restrictions, was compared to real production data (baseline) and tested across six scenarios. The best improvement over the baseline scenario resulted in a 3% increase in total biogas production, a 6% increase in total organic loading, a 13% increase in hot water use, and a one-day reduction in hydraulic retention time. Future work should focus on control studies using extended datasets and nonlinear models. Full article

The tip–tilt mirror (TTM) is an important component of adaptive optics (AO) to achieve beam stabilization and pointing tracking. In many practical applications, the information of accurate TTM dynamics, complete system state, and noise characteristics is difficult to achieve due to the lack of sufficient sensors, which then restricts the implementation of high precision tracking control for TTM. To this end, this paper proposes a new method based on noisy-output feedback Q-learning. Without relying on neural networks or additional sensors, it infers the dynamics of the controlled system and reference jitter using only noisy measurements, thereby achieving optimal tracking control for the TTM system. We have established a modified Bellman equation based on estimation theory, directly linking noisy measurements to system performance. On this basis, a fast iterative learning of the control law is implemented through the adaptive transversal predictor and experience replay technique, making the algorithm more efficient. The proposed algorithm has been validated with an application to a TTM tracking control system, which is capable of quickly learning near-optimal control law under the interference of random noise. In terms of tracking performance, the method reduces the tracking error by up to 98.7% compared with the traditional integral control while maintaining a stable control process. Therefore, this approach may provide an intelligent solution for control issues in AO systems. Full article

This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system’s state via indirect observations, alongside an efficient stochastic maximum principle-type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings. Full article

Relay channels capture the essence of several important communication scenarios such as sensor network and satellite communication. In this paper, first, we propose an efficient coding scheme for an additive white Gaussian noise (AWGN) relay channel in the presence of AWGN feedback, which generalizes the conventional scheme for the AWGN relay channel with noiseless feedback by introducing a lattice-based strategy to eliminate the impact of the feedback channel noise on the performance of the original scheme. Next, we further extend the proposed scheme to the multi-input single-output (MISO) fading relay channel (FRC) with noisy feedback. The key to this extension is to use a pre-coding strategy to transform the MISO channel into a single-input single-output (SISO) channel and applying a two-dimensional lattice coding strategy to deal with the feedback fading channel noise. Finally, we analyze the security performance of our proposed scheme in several cases, and the results of this paper are further illustrated by numerical examples. Full article

This review paper introduces the modeling, measurement, identification and control of direct current motors based on the state space modeling and the transfer function representation. These models are identified by real laboratory measurements, and the simulated results are compared with the measurements. Continuous-time and discrete-time PID (Proportional-Integral-Derivative) controllers, discrete-time state feedback and linear quadratic controllers are designed mathematically. The designed controllers are realized by the microcontroller Arduino UNO, and the behavior of the controllers is compared and analyzed. The noisy current signal has been measured by a discrete-time observer, steady-state Kalman filtering is also studied. The practical results of the implemented controllers support the theoretical results very well. Full article

While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training, which only utilizes noisy images and hinders further performance improvements In this work, we propose a novel dual-branch self-boosting network called DBSNet, which offers a straightforward and effective approach to image denoising. By leveraging task-dependent features, we exploit the intrinsic relationships between the two branches to enhance the effectiveness of our proposed model. Initially, we extend the classic Noise2Noise (N2N) architecture by adding a new branch for noise component prediction to the existing single-branch network designed for content prediction. This expansion creates a dual-branch structure, enabling us to simultaneously decompose a given noisy image into its content (clean) and noise components. This enhancement allows us to establish stronger constraint conditions and construct more powerful loss functions to guide the training process. Furthermore, we replace the UNet structure in the N2N network with the proven DnCNN (Denoising Convolutional Neural Network) sequential network architecture, which enhances the nonlinear mapping capabilities of the DBSNet. This modification enables our dual-branch network to effectively map a noisy image to its content (clean) and noise components simultaneously. To further improve the stability and effectiveness of training, and consequently enhance the denoising performance, we introduce a feedback mechanism where the network’s outputs, i.e., content and noise components, are fed back into the dual-branch network. This results in an enhanced loss function that ensures our model possesses excellent decomposition ability and further boosts the denoising performance. Extensive experiments conducted on both synthetic and real-world images demonstrate that the proposed DBSNet outperforms the unsupervised N2N denoising model as well as mainstream supervised models trained with supervised methods. Moreover, the evaluation results on real-world noisy images highlight the desirable generalization ability of DBSNet for practical denoising applications. Full article

Social recommendations typically utilize social relationships and past behaviors to predict users’ preferences. In real-world scenarios, the connections between users and items often extend beyond simple pairwise relationships. Leveraging hypergraphs to capture high-order relationships provides a novel perspective to social recommendation. However, effectively modeling these high-order relationships is challenging due to limited external knowledge and noisy feedback. To tackle these challenges, we propose a novel framework called self-supervised hypergraph learning for knowledge-aware social recommendation (SHLKR). In SHLKR, we incorporated three main types of connections: behavior, social, and attribute context relationships. These dependencies serve as the basis for defining hyperedges in the hypergraphs. A dual-channel hypergraph structure is created based on these relationships. Then, the hypergraph convolution is applied to model the high-order interactions between users and items. Additionally, we adopted a self-supervised learning task to maximize the consistency between different views. It helps to mitigate the model’s sensitivity to noisy feedback. We evaluated the performance of SHLKR through extensive experiments on publicly available datasets. The results demonstrate that leveraging hypergraphs for modeling can better capture the complexity and diversity of user preferences and interactions. Full article

Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller’s bandwidth. A variety of input trajectories can be used to drive a controller’s attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A benchmark feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system’s flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Benchmark feedforward whiplash compensation is additionally implemented for comparison. The method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present. Full article