Embedded Systems (United States)

ABSTRACT For high‐order systems, strong nonlinearities, parameter uncertainties and external disturbances pose significant control challenges. Several improved active disturbance rejection controller (ADRC) methods are proposed, including input modified ADRC (MADRC), time‐delay ADRC (DADRC) and Smith‐like ADRC (SLADRC). These improved ADRC methods are systematically compared with conventional proportional integral (PI) control and regular ADRC, focusing on key performance metrics such as tracking accuracy and disturbance rejection capability. The effectiveness of the proposed improved ADRC methods is validated via numerical simulations of a typical superheated steam temperature (SST) system. The integral absolute error (IAE) of ADRC, MADRC, DADRC and SLADRC shows a reduction of around 30, 50, 33 and 35, respectively, relative to that of PI. The outcomes of numerical simulations demonstrate that improved ADRC methods achieve satisfactory control performance, whereas MADRC stands out with the best overall performance. Thus, field application data show that compared with PI, MADRC exhibits significant reductions in key deviation metrics, with the maximum positive deviation decreasing by 33, the maximum negative deviation decreasing by 8, the average positive deviation decreasing by 25, the average negative deviation decreasing by 11 and the variance deviation decreasing by 18.

Wireless Sensor Networks (WSNs) are increasingly essential for Internet of Things (IoT) applications including smart agriculture, industrial automation, and environment monitoring. But the traditional software-based sensor nodes are power-hungry and suffer from high latency, which makes them unsuitable for scalability and lifetime. The paper introduces an RTOS-managed VLSI-based sensor node that combines real-time scheduling with hardware acceleration. The Real-Time Operating System (RTOS) schedules tasks, handles faults, and optimizes power, while VLSI co-processor takes care of computeintensive functions like filtering, compression, and encryption. The architecture shows <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 0}-\mathbf{4 0}$</tex> percentage power reduction and 50 percentage latency saving compared to software-only nodes. A case study of a temperature-humidity sensor proves the design. The findings ensure that the RTOS-VLSI hybrid nodes introduce an energy-efficient and scalable platform for future WSNs.

The battery management system (BMS) is the cornerstone of the safe operation of electric vehicles (EVs), and its stable operation relies heavily on the accuracy of the battery current sensor. In this paper, a novel method for current sensor fault diagnosis in lithium-ion batteries is presented. First, a fractional-order (FO) battery model is proposed, and a hybrid particle swarm optimization algorithm with a cross-learning strategy is developed for model parameter identification. Second, a FO observer is designed for state of charge (SOC) estimation, and the stability of the observation error system is verified. Finally, by accurately estimating the change in SOC caused by a fault current and employing residual analysis, current sensor faults are identified. Experimental analysis, carried out under different operating conditions, reveals that the proposed method detects current sensor faults more accurately and quickly than the general open-circuit voltage method. Moreover, it can adapt to varying operating conditions and temperatures.

This study presents a novel sensor-based system for human vital sign estimation through a glass barrier using Texas Instruments (TI) 77 GHz AWR1843BOOST Frequency Modulated Continuous Wave (FMCW) radar. To improve signal fidelity and ensure precise extraction of physiological parameters, the signal processing pipeline incorporates beamforming, Variational Mode Decomposition (VMD), and Recursive Bayesian Estimation (RBE). Experiments were conducted across distances of 0.4 m to 0.7 m through glass barrier. The system achieved an Mean Absolute Error (MAE) of 1.93 breaths per minute for respiratory rate and 8.47 beats per minute for HR, with average accuracies of 83.74% and 91.41%, respectively. The results highlight the system's potential for reliable, hygienic, non-invasive monitoring in sensitive settings like neonatal intensive care units (NICUs) and infection control wards, where contact-based methods are risky.

In this paper, we propose a novel signal processing method for road surface roughness classification exclusively from wheel speed sensor signals. Road surface quality has a significant impact on fuel consumption and driving safety. Traditionally, it has been measured using specially equipped vehicles and, more recently, shared via cloud-based infrastructure; however, such data can be unavailable or quickly become outdated, making onboard solutions essential. We analyzed a large wheel speed sensor dataset from various test maneuvers to determine how road surface roughness influences spectral characteristics under different conditions, including changes in speed, tire pressure, payload, and tire type. The proposed road surface roughness classifier uses only wheel speed sensor signals. It selects signal segments appropriate for processing based on driving conditions and computes their order spectra. The number and relative power of the spectral peaks within the identified interval of interest of the order spectrum are related to road surface roughness. The implemented classifier is capable of distinguishing between rough and smooth road surfaces based on the number of peaks in the interval of interest. The overall accuracy of the implemented road surface roughness classifier was 87.4%.

Unmanned aerial vehicles (UAVs), commonly referred to as drones, have emerged as a transformative technology with wide-ranging applications across civilian, commercial, and military domains. Their ability to operate in dynamic and often inaccessible environments without onboard human pilots offers unparalleled flexibility, mobility, and cost-efficiency. From aerial surveillance and environmental monitoring to disaster response and wireless communication enhancement, UAVs are increasingly being integrated into complex systems that demand intelligent sensing, real-time data processing, and reliable connectivity. The rapid advancements in autonomous control, wireless networking, and artificial intelligence (AI) have further propelled the development of UAV-enabled systems, enabling more sophisticated missions and multi-agent coordination. As UAV adoption accelerates, understanding their underlying technologies and operational models becomes essential for optimizing performance, ensuring safety, and supporting future innovations.

This chapter studies the threat that an aerial eavesdropper can pose to terrestrial wireless communications, from an information-theoretic point of view. The achievable ergodic and the average ϵ -outage secrecy rates with no channel state information at the transmitter (i.e., with no CSIT) are analyzed for a transmitter-receiver pair on the ground, in the presence of an aerial eavesdropper which flies a random trajectory following a smooth turn (ST) mobility model in a 3D space. The ST mobility model induces a uniform distribution (of the eavesdropper's waypoints) within the considered 3D volume. Closed-form asymptotic approximations of the achievable secrecy rates are derived based on the almost sure convergence and nontrivial mathematical manipulations.

The rise of chiplet-based system-on-chip (SoC) architectures enables unprecedented modularity and scalability across modern computing platforms. Chiplets have become essential in addressing the growing performance demands of both highperformance and embedded systems. However, this wide adoption introduces new attack surfaces, as heterogeneous chiplets sourced from untrusted vendors may host stealthy hardware Trojans. This paper presents the design and simulation of a two-chiplet AES encryption-decryption system communicating over the Bunch-of-Wires (BoW) protocol, into which multiple hardware Trojans from the Trust-Hub benchmark suite were integrated. The inserted Trojans were triggered by rare input conditions and evaluated through functional verification, structural analysis, and switching activity monitoring. A weighted Suspicion Score metric was introduced to unify the results from static inspection, DRC, timing, and switching activity analyses, enabling quantitative risk classification of Trojan presence. Experimental results demonstrated successful detection of several benchmark Trojans through measurable timing, logic, and power anomalies.

In this paper, we propose a nonlinear model predictive controller (NMPC) that employs a feedforward neural network as the prediction model, with the underlying optimization problem solved separately using either Particle Swarm Optimization (PSO) or Teaching-Learning-Based Optimization (TLBO). A quadrotor system is chosen as the application platform due to its underactuated, multi-input multi-output (MIMO) nature and highly nonlinear dynamics, conditions under which NMPC is particularly effective, especially in handling constraints. A Type-2 fuzzy logic controller and a PSO-optimized PID controller are used to compare the performance of the suggested neural network-based NMPC framework.