New energy storage devices such as batteries and supercapacitors are widely used in various fields because of their irreplaceable excellent characteristics. Because there are relatively few ...monitoring parameters and limited understanding of their operation, they present problems in accurately predicting their state and controlling operation, such as state of charge, state of health, and early failure indicators. Poor monitoring can seriously affect the performance of energy storage devices. Therefore, to maximize the efficiency of new energy storage devices without damaging the equipment, it is important to make full use of sensing systems to accurately monitor important parameters such as voltage, current, temperature, and strain. These are highly related to their states. Hence, this paper reviews the sensing methods and divides them into two categories: embedded and non-embedded sensors. A variety of measurement methods used to measure the above parameters of various new energy storage devices such as batteries and supercapacitors are systematically summarized. The methods with different innovative points are listed, their advantages and disadvantages are summarized, and the application of optical fiber sensors is emphasized. Finally, the challenges and prospects for these studies are described. The intent is to encourage researchers in relevant fields to study the early warning of safety accidents from the root causes.
Cloud data centers (DCs) consume large amounts of energy and contribute significantly to environmental concerns. Furthermore, with the advent of 5G and B5G networks, increasingly software-oriented ...and becoming highly dependent on cloud computing, it becomes imperative to optimize their energy consumption. Thus, in this study, we present a virtual machine placement algorithm that minimizes the energy consumption of a cluster of server machines. Our solution is embodied through the use of sensors embedded inside physical server machines, enabling the introduction of new features for sensitive thermal awareness and proactive hot spot avoidance. Leveraging this significantly enhanced feature space, we implement data-driven predictive machine learning models along with a heuristic placement algorithm (CPP), enabling proactive VM placements that are both energy-aware and thermal-aware. Indeed, experiments carried out on a cluster of physical server machines demonstrate high performance by both the ML models and the placement algorithm (CPP). Compared with the best baseline algorithm, our solution reduced power consumption and temperature by 7% and 2%, respectively, while avoiding hot spots and maintaining efficient load distribution, thereby reducing the overhead of physical machines by 28%.
•Cloud data centers (DCs) consume large amounts of energy and contribute significantly to environmental concerns. Furthermore, with the advent of 5G and B5G networks, increasingly software-oriented and becoming highly dependent on cloud computing, it becomes imperative to optimize their energy consumption.•Thus, in this study, we present a virtual machine placement algorithm that minimizes the energy consumption of a cluster of server machines.•Our solution is embodied in the use of sensors integrated inside physical server machines, allowing the introduction of new functionalities for sensitive thermal awareness and proactive avoidance of hot spots.•Leveraging this significantly enhanced feature space, we implement data-driven predictive machine learning models along with a heuristic placement algorithm (CPP), enabling proactive placements of virtual machines that take into account both energy and heat. Indeed, the experiments carried out on a cluster of physical server machines demonstrate high performance both in terms of the ML models and the placement algorithm (CPP).•Compared with the best baseline algorithm, our solution reduced power consumption and temperature by 7% and 2% respectively, while avoiding hot spots and maintaining efficient load distribution, thereby reducing the overhead of physical machines by 28%.
Display omitted
•A wearable glove-embedded sensor for therapeutic drug monitoring of paracetamol, paroxetine, ethinylestradiol and uric acid.•Detection direct by wearable glove-embedded sensor ...touching sweaty skin.•Selective, cross-talk free on-site detection of analgesic, antidrepressant and hormone.•Tracking closely analgesic, antidrepressant and hormones concentrations in a single drop of sweat sample.•Timely analytical tool for personalized medicine toward ideal dose delivery.
Wearable sensing technology may fulfill the requirements of precision medicine, but the use of on-body devices for rapid, selective screening of therapeutic drugs is relatively new. In this paper, we report on wearable glove-embedded sensors for non-invasive and selective determination of therapeutic drugs and a biomarker in sweat samples. Electrochemical sensing was made with an array of sensors printed on four fingers of a plastic glove. Uric acid was detected using the index finger functionalized with carbon black with a limit of detection of 1.37 × 10–6 mol L–1. Paracetamol and paroxetine were detected using the middle and ring fingers coated with Printex Carbon with limits of detection of 2.47 × 10–7 and 4.93 × 10–7 mol L–1, respectively. Ethinylestradiol (EE2) detection was performed with a pre-treated screen-printed carbon electrode on the little finger with limit of detection of 9.35 × 10–7 mol L–1. The high sensitivity and selectivity achieved with the glove-embedded sensors were possible due to a judicious choice of sensing layers and optimized working conditions for differential pulse voltammetry. Monitoring of the analytes was carried out in sweat samples with suitable recovery between 90 and 110%. The glove-embedded sensors were robust against multiple flexion cycles, stable and reproducible, with no response to interference from other molecules in sweat. They may be extended to detect other targets for on-site analysis, in addition to applications in environmental and water samples.
Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the ...video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique "identity" electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security.
Gait is a biometric trait that can allow user authentication, though it is classified as a “soft” one due to a certain lack in permanence and to sensibility to specific conditions. The earliest ...research relies on computer vision, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, has spurred a different research line. In fact, they are able to capture the dynamics of the walking pattern through simpler one-dimensional signals. This capture modality can avoid some problems related to computer vision-based techniques but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, many factors - the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques - contribute to making this biometrics attractive and suggest continuing investigating. This survey provides interested readers with a reasoned and systematic overview of problems, approaches, and available benchmarks.
A new wearable electrochemical sensor for monitoring the pH of wounds is introduced. The device is based on the judicious incorporation of a screen‐printed pH potentiometric sensor into bandages. The ...fabrication of this sensor, which uses an electropolymerized polyaniline (PANi) conducting polymer for pH sensing, combines the screen‐printing fabrication methodology with all‐solid‐state potentiometry for implementation of both the reference and the working electrodes. The pH bandage sensor displays a Nernstian response over a physiologically relevant pH range (5.5–8), with a noteworthy selectivity in the presence of physiological levels of most common ions. The bandage‐embedded sensor can track pH fluctuations with no apparent carry‐over effect. The sensor displays good resiliency against mechanical stress, along with superior repeatability and reproducibility. The in vitro performance of the device was successfully evaluated using buffer solutions emulating the composition of a wound. The novel pH‐sensitive bandages facilitate new avenues towards the realization of telemedicine.
Embedded sensors (ESs) are used in smart materials to enable continuous and permanent measurements of their structural integrity, while sensing technology involves developing sensors, sensory ...systems, or smart materials that monitor a wide range of properties of materials. Incorporating 3D-printed sensors into hosting structures has grown in popularity because of improved assembly processes, reduced system complexity, and lower fabrication costs. 3D-printed sensors can be embedded into structures and attached to surfaces through two methods: attaching to surfaces or embedding in 3D-printed sensors. We discussed various additive manufacturing techniques for fabricating sensors in this review. We also discussed the many strategies for manufacturing sensors using additive manufacturing, as well as how sensors are integrated into the manufacturing process. The review also explained the fundamental mechanisms used in sensors and their applications. The study demonstrated that embedded 3D printing sensors facilitate the development of additive sensor materials for smart goods and the Internet of Things.
Conventional machines rely on rigid, centralized electronic components to make decisions, which limits complexity and scaling. Here, we show that decision making can be realized on the material-level ...without relying on semiconductor-based logic. Inspired by the distributed decision making that exists in the arms of an octopus, we present a completely soft, stretchable silicone composite doped with thermochromic pigments and innervated with liquid metal. The ability to deform the liquid metal couples geometric changes to Joule heating, thus enabling tunable thermo-mechanochromic sensing of touch and strain. In more complex circuits, deformation of the metal can redistribute electrical energy to distal portions of the network in a way that converts analog tactile 'inputs' into digital colorimetric 'outputs'. Using the material itself as the active player in the decision making process offers possibilities for creating entirely soft devices that respond locally to environmental interactions or act as embedded sensors for feedback loops.
Soft robotics has attained significant attention in recent years due to their infinite degree of freedom, adaptability, and safer interaction with humans and objects in various applications, such as ...robotics, biomedical devices, search and rescue operations, and wearable technology. However, the soft pneumatic actuator (SPA) is one of the most commonly and frequently utilized actuators in soft robotics due to its higher output at lower energy consumption. Further, an important development was the embedding/integration of soft and flexible sensors within the SPAs. This review article provides a comprehensive review of the frequently utilized SPA designs (McKibben, Pneu-Nets, and material jamming-based actuators), soft sensors (strain, pressure, temperature, tactile, flex, optical, and magnetic sensors), and SPAs with integrated or embedded soft sensors. Further, various emerging trends and breakthroughs in the SPA-sensors integration along with the limitations and challenges are discussed. Finally, it is found that there is an enormous amount of potential for revolutionizing the robotic and associated industries through the integration or embedding of soft sensors into SPAs. Even though there has been a lot of progress, there are still various challenges that need to be resolved and are provided in the future work section.
Display omitted
PDF
