Ferroelectric tunnel junctions (FTJs) are promising nonvolatile memory devices for in‐memory computing due to their nonfilamentary switching behavior, low operating current, and multilevel conductance programmability. In this article, the influence of postmetallization annealing (PMA) temperature on HZO‐based FTJs is investigated for stable ferroelectric switching characteristics. This annealing process promotes orthorhombic phase crystallization and reduces interfacial traps, enabling saturated polarization from the initial cycle and endurance exceeding 104 cycles. A 48 × 48 FTJ crossbar array is fabricated to evaluate array‐level functionality. Half‐bias operation is successfully demonstrated, confirming that unselected cells remained stable without interference from neighboring cells. Uniform 3‐bit conductance programming is achieved across all 2,304 cells, exhibiting clearly separated states. Furthermore, vector–matrix multiplication tests verified accurate array‐level operation, yielding an output error deviation as low as 0.97%. Finally, the final fully connected layer of a CIFAR‐10 classifier is implemented directly on the crossbar array, achieving an 88.3% classification accuracy, closely matching the 88.5% software baseline. These findings underscore the potential of HZO‐based FTJ crossbar arrays as energy‐efficient platforms for in‐memory computing.
Computer engineering. Computer hardware, Control engineering systems. Automatic machinery (General)
Nishat Tasnim, Mirza M. A. Tunur, Fahad Arefin
et al.
AI‐powered monitoring platforms can significantly enhance the accessibility and responsiveness of water quality assessment in decentralized and resource‐limited settings. Conventional methods for detecting heavy metal ions, such as atomic absorption spectroscopy (AAS), offer high accuracy but require expensive instrumentation, trained personnel, and laboratory infrastructure, limiting their use in field applications. Here, SmartDetectAI, a low‐cost, portable, AI‐powered web application designed for rapid, on‐site colorimetric detection of heavy metal ions in water is presented. The system integrates silver nanoparticles (AgNPs) prepared from plant extract with a custom‐built imaging chamber and a web‐based application (web app) for automated and remote analysis. Supported by a computer vision model (YOLOv8n) for region detection and a machine learning algorithm (XGBoost) for concentration estimation, SmartDetectAI enables automated, real‐time quantification of mercury‐ and cadmium‐based species, which are the predominant aqueous forms under near‐neutral pH conditions. Users capture sensor images with a smart device and receive result outputs through an intuitive graphical interface hosted on a Flask‐based server. Field validation using pond water samples spiked with 1 and 10 μM Cd2+ shows strong agreement with standard AAS measurements, achieving an average predictive accuracy of ≈84%.
Computer engineering. Computer hardware, Control engineering systems. Automatic machinery (General)
Flexible tactile sensors inspired by human skin enable significant potential applications, including e‐skin for intelligent robotics, wearable healthcare devices, and human–machine interfaces. Although the application of liquid metals (LM) and their composites has improved the stretchability of various tactile sensors, there remains a considerable gap between the performance of current flexible tactile sensors and human skin, attributed to their limited sensitivity and narrow working range. In this work, a liquid metal droplet (LMD)‐based flexible tactile sensor that achieves both high‐pressure sensitivity of 3 × 10−2 kPa−1 and a wide working range from 50 Pa to 1.2 MPa is proposed. The novel sensor consists of an LMD array in which each pair of droplets is connected by an electrolyte solution within the polydimethylsiloxane microchannel grid. It is demonstrated that the 2‐LMD sensor is capable of detecting human motion and physiological signals, while the electrical impedance tomography‐based 3.5 × 3.5 cm sensor can detect the shape and position of single‐point, multipoint, and other complex contact using only eight evenly distributed electrodes along the sensor's edge. These findings highlight the promising potential of the sensors in future robotic and wearable electronic applications.
Computer engineering. Computer hardware, Control engineering systems. Automatic machinery (General)
Daniel Rodríguez-Nieto, Marta Ojeda, Eduardo Navas
et al.
La arquitectura software es un componente crucial en cualquier sistema robótico autónomo, ya que define la estructura organizativa y las interacciones de los diferentes módulos que lo integran. Para que un sistema robótico pueda ejecutar de forma autónoma diversas tareas, se requieren procesos variados, como percibir el entorno, representar conocimientos, tomar decisiones y planificar movimientos. Si bien el desarrollo de cada uno de estos procesos es fundamental, su integración en una arquitectura funcional para su implementación también lo es. Esta integración tiene profundas implicaciones en la gestión de recursos, la adaptabilidad a diferentes entornos y tareas, la flexibilidad para modificar o expandir las funcionalidades y hacer frente a nuevos requerimientos, y la facilidad para el mantenimiento y la actualización del sistema. Por ello, en este artículo se presenta la arquitectura software diseñada para controlar, comunicar e integrar los distintos módulos que componen un bimanipulador móvil, destacando entre sus principales ventajas, la facilidad para depurar errores y llevar a cabo pruebas de nuevas aplicaciones sin el riesgo inherente de dañar el equipo físico. Para demostrar la viabilidad de la propuesta, la implementación de la arquitectura se valida mediante su aplicación al sistema robótico de manipulación dual HortiRobot, concebido para realizar varias de las tareas implicadas en el ciclo de vida de los cultivos agrícolas.
Control engineering systems. Automatic machinery (General)
Antoine P. Leeman, Johannes Köhler, Florian Messerer
et al.
System level synthesis enables improved robust MPC formulations by allowing for joint optimization of the nominal trajectory and controller. This paper introduces a tailored algorithm for solving the corresponding disturbance feedback optimization problem for linear time-varying systems. The proposed algorithm iterates between optimizing the controller and the nominal trajectory while converging q-linearly to an optimal solution. We show that the controller optimization can be solved through Riccati recursions leading to a horizon-length, state, and input scalability of $\mathcal{O}(N^2 ( n_x^3 +n_u^3))$ for each iterate. On a numerical example, the proposed algorithm exhibits computational speedups by a factor of up to $10^3$ compared to general-purpose commercial solvers.
Yoav Matia, Gregory H. Kaiser, Robert F. Shepherd
et al.
Herein, complex motion in soft, fluid‐driven actuators composed of elastomer bladders arranged around a neutral plane and connected by slender tubes is demonstrated. Rather than relying on complex feedback control or multiple inputs, the motion is generated with a single pressure input, leveraging viscous flows within the actuator to produce nonuniform pressure between bladders. Using an accurate predictive model coupling with a large deformation Cosserat rod model and low‐Reynolds‐number flow, all dominating dynamic interactions including extension and curvature are captured with two governing equations. Given insights from this model, five design elements are described and demonstrated in practice. By choosing the relative timescales between the solid, fluid, and input pressure cycles, the tip of the actuator can obtain almost any desired trajectory and can be placed anywhere temporarily within its 2D workspace. Finally, the benefits of viscous‐driven soft actuators are showcased in a six‐legged untethered robot able to walk 0.05 body lengths per second. The foundation is laid for a new class of morphologically intelligent, soft robotic actuators that enables complex deformations and multifunctionality without explicit drivers; whereby generating nonuniform pressure distributions, their infinite degrees of freedom can be exploited.
Computer engineering. Computer hardware, Control engineering systems. Automatic machinery (General)
E-health care is an emerging field where health services and information are delivered and offered over the Internet. So the health information of the patients communicated over the Internet has to protect the privacy of the patients. The patient information is embedded into the health record and communicated online which also induces degradation to the original information. So, in this article, a zero watermarking scheme for privacy protection is proposed which protects the privacy and also eliminates the degradation done during embedding of patient information into the health record. This method is based on simple linear iterative clustering (SLIC) superpixels and partial pivoting lower triangular upper triangular (PPLU) factorization. The novelty of this article is that the use of SLIC superpixels and PPLU decomposition for the privacy protection of medical images (MI). The original image is subjected to SLIC segmentation and non-overlapping high entropy blocks are selected. On the selected blocks discrete wavelet transform (DWT) is applied and those blocks undergo PPLU factorization to get three matrices, L, U and P, which are lower triangular, upper triangular and permutation matrix respectively. The product matrix [Formula: see text] is used to construct a zero-watermark. The technique has been experimented on the UCID, BOWS and SIPI databases. The test results demonstrate that this work shows high robustness which is measured using normalized correlation (NC) and bit error rate (BER) against the listed attacks.
Control engineering systems. Automatic machinery (General), Automation
Aditya Dave, Nishanth Venkatesh, Andreas A. Malikopoulos
In this paper, we investigate discrete-time decision-making problems in uncertain systems with partially observed states. We consider a non-stochastic model, where uncontrolled disturbances acting on the system take values in bounded sets with unknown distributions. We present a general framework for decision-making in such problems by using the notion of the information state and approximate information state, and introduce conditions to identify an uncertain variable that can be used to compute an optimal strategy through a dynamic program (DP). Next, we relax these conditions and define approximate information states that can be learned from output data without knowledge of system dynamics. We use approximate information states to formulate a DP that yields a strategy with a bounded performance loss. Finally, we illustrate the application of our results in control and reinforcement learning using numerical examples.
Encrypted control is a framework for the secure outsourcing of controller computation using homomorphic encryption that allows to perform arithmetic operations on encrypted data without decryption. In a previous study, the security level of encrypted control systems was quantified based on the difficulty and computation time of system identification. This study investigates an optimal design of encrypted control systems when facing an attack attempting to estimate a system parameter by the least squares method from the perspective of the security level. This study proposes an optimal $H_2$ controller that maximizes the difficulty of estimation and an equation to determine the minimum security parameter that guarantee the security of an encrypted control system as a solution to the design problem. The proposed controller and security parameter are beneficial for reducing the computation costs of an encrypted control system, while achieving the desired security level. Furthermore, the proposed design method enables the systematic design of encrypted control systems.
This survey is focused on certain sequential decision-making problems that involve optimizing over probability functions. We discuss the relevance of these problems for learning and control. The survey is organized around a framework that combines a problem formulation and a set of resolution methods. The formulation consists of an infinite-dimensional optimization problem. The methods come from approaches to search optimal solutions in the space of probability functions. Through the lenses of this overarching framework we revisit popular learning and control algorithms, showing that these naturally arise from suitable variations on the formulation mixed with different resolution methods. A running example, for which we make the code available, complements the survey. Finally, a number of challenges arising from the survey are also outlined.
Danilo Obradovic, Mehrdad Ghandhari, Robert Eriksson
Frequency Containment Reserves might be insufficient to provide an appropriate response in the presence of large disturbances and low inertia scenarios. As a solution, this work assesses the supplementary droop frequency-based Emergency Power Control (EPC) from HVDC interconnections, applied in the detailed Nordic Power System model. EPC distribution and factors that determine the EPC performance of an HVDC link are the focus of interest. The main criteria are the maximum Instantaneous Frequency Deviation and used EPC power. The presented methodology is motivated based on the theoretical observation concerning linearized system representation. However, the assessed and proposed properties of interest, such as provided EPC active and reactive power, their ratio, and energy of total loads and losses in the system due to the EPC, concern highly nonlinear system behavior. Finally, based on the obtained study, remarks on the pragmatical importance of the EPC distribution to the frequency nadir limitation are provided.