Dear Colleagues, Following the success of the second edition of our Special Issue titled “Ultrasonic Imaging and Sensors II” (https://www.mdpi.com/journal/sensors/special_issues/0P1EXS34JB), we once again invite our colleagues from across the world to contribute their expertise, insights, and findings in the form of original research articles and reviews for a new Special Issue, titled “Ultrasonic Imaging and Sensors—Third Edition”. This Special Issue is open to new research and review papers in relevant domains, including, but not limited to, the following: New materials for ultrasound transducers;Transducer design for NDT and medical applications;Physical acoustics;Analog and digital ultrasound electronics;New beamforming methods and imaging modalities;New NDT and medical ultrasound applications;Ultrasonic image processing and AI techniques. We highly appreciate your contributions to this Special Issue, which we hope will provide a comprehensive overview of recent advances in all fields within ultrasonic imaging. Dr. Jorge CamachoDr. Linas SvilainisDr. Tomás Gómez álvarez-ArenasGuest Editors

Dear Colleagues, Advances in soft materials and flexible electronics are promoting the development of biointegrated sensors and robotic systems that can interface closely with the human body. This Special Issue focuses on the design of soft bioelectronic sensors and robotic interfaces that provide natural and seamless interaction with human users, supporting applications such as physiological signal monitoring, adaptive actuation, and intelligent feedback. We welcome original research on soft and stretchable sensors that capture diverse biosignals (e.g., EEG, EMG, PPG, SCG), wearable systems for continuous health monitoring, and conformable devices for robust human–machine interaction. Studies that address the application of soft actuators, biohybrid robotics, and closed-loop control strategies in rehabilitation, assistive technologies, and neuroadaptive systems are also of interest. We particularly welcome contributions that integrate signal processing, machine learning, and system-level design for applications in digital health, AR/VR environments, and brain–computer interfaces (BCIs). By compiling interdisciplinary research spanning materials science, biomedical engineering, and robotics, this Special Issue aims to showcase recent progress and future directions in human-centered soft robotic technologies. Dr. Hodam KimDr. Woon-Hong YeoGuest Editors

Dear Colleagues, During the last decades the human society has experimented a huge technological development. As a result, several sensors are available to monitor and assess motor functioning both in healthy and people with health troubles. This sprawl of sensors to evaluate movement provide the opportunity to use it not only as a way to determine the current motor state of people but also to be used as a training involved tool to improve motor function. Therefore, the main aim of this special issue is to publish scientific evidence on the use of different sensors to promote motor learning. Potential topics include but are not limited to: Clinical use of sensors in motor function recovery.; Sensors in educational contexts to facilitate motor learning.; Motion sensors applied in sports as training tool.; Development of wearable sensors to facilitate motor learning.; New strategies to evaluate motor learning through sensing technology.; Dr. Xavier García-MassóDr. Israel Villarrasa-Sapi?aDr. Cristina MenescardiGuest Editors

Dear Colleagues, This Special Issue invites original research articles, comprehensive reviews, and short communications that focus on theprocessing of medical images, from acquisition to analysis. With the goal of enhancing diagnostic accuracy, treatment planning, and clinical decision-making. We seek contributions that advance the state of the art in computational methods and algorithms used to process medical imaging data across various modalities. We welcome submissions in, but not limited to, the following areas: Medical Image Acquisition and Reconstruction Algorithms: innovative computational methods for improving image quality during or after acquisition, including motion correction, compressed sensing, and deep learning-based reconstruction.; Image Enhancement Techniques: algorithms for denoising, artifact removal, contrast enhancement, and super-resolution to improve the clarity and diagnostic value of medical images.; Segmentation and Registration: advanced techniques for delineating anatomical structures and pathologies, as well as aligning images across time points or modalities.; Quantitative Image Analysis and Interpretation: methods for extracting meaningful features, performing statistical analysis, and applying machine learning or deep learning for classification, detection, and prognosis.; Multi-Modal Image Fusion and Integration: computational approaches for combining data from different imaging modalities (e.g., MRI, CT, PET) to provide richer diagnostic insights.; Large Language Models (LLMs) and Vision-Language Models (VLMs): applications of LLMs and multi-modal VLMs in medical image processing, including report generation, image-text alignment, clinical decision support, and multi-modal reasoning.; Computer-Aided Diagnosis (CAD) and Decision Support: development of intelligent systems that assist clinicians in interpreting images and planning treatments.; Review articles should provide a balanced and up-to-date overview of specific image processing techniques or applications in medical imaging, highlighting key developments and future directions. We look forward to your participation in this Special Issue. For any enquiries, please contact the Special Issue editor, Silvia Li, atsilvia.li@mdpi.com. Dr. Saed MoradiGuest Editor

Dear Colleagues, Quantitative analysis of gas concentrations became a topic of great importance in the recent century. This occurred because of the high demand for reliable sensors to implement in numerous fields, including environmental monitoring, medical sensors, security control and industrial process monitoring. Among the various techniques, photoacoustic spectroscopy (PAS) is proving to be an excellent alternative, compared to bulkier and high-performing devices, in terms of quantification of gas species in a specific matrix. This is due to its easy implementation and transport, small investigation volumes and absence of background signal. Detection sensitivities of the order of part per billion or lower are easily achievable, in short integration times, for the majority of light molecules using integrated, low-power instrumentation. The PAS technique, on the one hand, enjoys excellent accuracy, especially through recent studies aimed at understanding and mitigating non-linearities due to non-radiative relaxation. On the other hand, through the use of innovative materials and additive technologies, such as 3D printing, this technique is becoming particularly approachable at a low cost, paving the way for large-scale use in many fields. Dr. Stefano Dello RussoGuest Editor

Dear Colleagues, Multimodal sensing is rapidly reshaping the landscape of brain–computer interfaces (BCIs) and human–robot interaction (HRI), enabling systems that are more adaptive, intuitive, and human-centered. By integrating neural, physiological, biomechanical, and environmental signals, next-generation intelligent systems can move beyond single-modality limitations to achieve robust perception, shared autonomy, and seamless interaction between humans and machines. Advances in wearable and non-invasive sensing technologies—such as EEG, EMG, ECG, GSR, motion capture, vision, and tactile sensing—are opening new possibilities for decoding intent, affect, and motor states in real time. These developments are driving transformative applications across neurorehabilitation, assistive robotics, immersive VR/AR, collaborative robotics, and mental health technologies. However, significant challenges remain in multimodal data fusion, real-time inference, robustness, personalization, interpretability, and closed-loop control. This Special Issue focuses on multimodal sensing frameworks, algorithms, and applications that advance BCIs and HRI, including, but not limited to, the following: Multimodal neural and physiological sensing for BCIs;; Sensor fusion and representation learning for intent and state decoding;; Affective, cognitive, and motor state estimation;; Human–robot collaboration and shared autonomy;; Wearable and unobtrusive sensing technologies;; Multimodal perception for assistive and rehabilitative robotics;; Closed-loop and adaptive BCI-robot systems;; VR/AR-based embodied interaction and therapy platforms;; Explainable and interpretable multimodal AI for HRI.; We invite original research articles, review papers, and short communications that present theoretical advances, methodological innovations, and real-world applications at the intersection of multimodal sensing, BCIs, and human–robot interaction. Dr. Ramana Kumar VinjamuriGuest Editor

Dear Colleagues, The advancements in sensor technologies have significantly enhanced the capabilities of fault diagnosis across various industries. These technologies enable the precise detection and analysis of system anomalies, leading to improved reliability and efficiency. The integration of sensors with advanced analytical methods offers substantial benefits in monitoring and controlling critical systems, ranging from machinery and vehicles to complex industrial processes. This Special Issue aims to consolidate the latest research and developments in fault diagnosis based on sensing and control systems. We seek to explore innovative approaches and methodologies that leverage intelligent sensing, multisensor fusion, predictive maintenance, real-time fault detection, adaptive control systems, and the application of machine learning and artificial intelligence in fault diagnosis. We invite contributions that delve into theoretical frameworks, computational models, experimental studies, and practical implementations across various engineering domains. We encourage submissions on a broad range of issues, including, but not limited to, the following: Intelligent sensing;; Multisensor fusion;; Predictive maintenance;; Real-time fault detection;; Adaptive control systems;; Machine learning and artificial intelligence in fault diagnosis;; Sensor-based monitoring systems.; By showcasing cutting-edge research in these areas, this Special Issue aims to foster academic exchange and drive advancements in the field of sensor-based fault diagnosis and control systems. Dr. Chengwei LiGuest Editor

Dear Colleagues, The rapid evolution of the Internet of Things (IoT) has driven an increasing demand for efficient and reliable communication networks to support the growing number of connected devices. IoT applications have expanded to remote or hard-to-reach areas where terrestrial networks are unavailable, making satellite communication networks an ideal solution to provide coverage across vast geographical regions. However, the inherent characteristics of satellite technologies introduce several challenges for IoT integration. High latency, particularly with geostationary satellites, can hinder real-time data transmission for time-sensitive IoT applications. Additionally, their limited bandwidth complicates the handling of large volumes of data generated by IoT sensors, and power efficiency is crucial for battery-powered devices in remote locations, both of which make it very challenging to overcome the significant losses faced due to the distance to the satellite. Harsh environmental conditions in remote areas can cause interference, affecting signal integrity. Moreover, the high cost of deploying satellite infrastructure and the limited availability of affordable services pose significant barriers to implementing IoT solutions in these regions. This Special Issue aims to explore the latest advancements and innovative solutions leveraging satellite communication systems for IoT applications. Contributions will focus on integrating satellite networks with IoT sensor technologies, addressing challenges such as low-latency communication, energy efficiency, network optimization, and data management. Potential topics include but are not limited to satellite-based IoT sensor networks for various applications such as agriculture, environmental monitoring, smart cities, and disaster management. We are also interested in overviews or surveys and analyses to promote the research and industry on the latest trends in IoT-based satellite applications. Dr. Victor Monzón BaezaGuest Editor

Dear Colleagues, Sensors comprise environmental-facing components in electronic control systems that utilize closed-loop control for the safe and reliable operation of the system being monitored. Some leading application areas that currently utilize such sensor systems include consumer products, healthcare, transportation, energy generation/management, and other applications, where the environmental conditions are compatible with silicon-based electronics. Technology advancements have progressed to the point that sensor systems are now being developed for applications where silicon-based electronics are not well suited due to environmental conditions. Examples of these harsh environments include high temperatures, high radiation, harsh chemicals, high pressure, high mechanical wear, and extreme vibration. For such applications, sensors and electronics based on wide-bandgap semiconductors are being developed. This Special Issue is focused on the sensors and associated electronics required for sensor systems that can operate under harsh environmental conditions. Specifically, we are interested in submissions that focus on electronic devices, circuits, sensors, and sensing systems that can operate in high temperature, high radiation, harsh chemical, extreme mechanical, and other environments that influence the direction of this important and rapidly emerging technology. This Special Issue aims to feature systems based on wide-bandgap semiconductors, but papers that describe approaches that utilize electronics-based silicon and other materials are also welcome to be submitted. Dr. Maximilian C. ScardellettiGuest Editor

Dear Colleagues, This Special Issue welcomes contributions that employ any innovative solution based on proximal or remote sensing aimed at being used in the site-specific management of any agroforestry system. Such approaches can, for instance, facilitate the delineation of homogeneous zones and encourage the adoption of precision agriculture strategies. Manuscripts may focus on applications in agricultural systems as well as in pastures and grasslands. Suitable topics include, but are not limited to, generation and refinement of targeted soil and/or plant information; data modelling and interpretation; applications of such information in various fields such as agriculture, forestry, natural resource management, and climate change mitigation; and the development of soil and/or plant information systems at multiple spatial scales. Special emphasis will be placed on research that employs or develops innovative data integration methodologies, studies that make use of novel proximal or remote sensing datasets, and research that delivers practical outcomes for local stakeholders. This includes tools for GIS-based planning and decision support aimed at improving field management and sustainability. Prof. Dr. Francisco Jesús Moral GarcíaProf. Dr. Jo?o Manuel Pereira Ramalho SerranoProf. Dr. Francisco Javier Rebollo CastilloGuest Editors