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Frequent flooding seriously affects people's safety and economic construction, and assessing the inundation probability can help to strengthen the capacity of emergency management of floods. There are currently two general means of flood sensing: physical and social. Remote sensing data feature high reliability but are often unavailable in disasters caused by persistent heavy rainfall. Social media is characterized by high timeliness and a large data volume but has high redundancy and low reliability. The existing studies have primarily relied on physical sensing data and have not fully exploited the potential of social media data. This paper combines traditional physical sensing data with social media and proposes an integrated physical and social sensing (IPS) method to estimate the probability distribution of flood inundation. Taking the "7·20" Henan rainstorm in 2021 and the study area of Xinxiang, China, as a case study, more than 60,000 messages and 1900 images about this occurrence were acquired from the Weibo platform. Taking filtered water depth points with their geographic location and water depth information as the main input, the inverse distance attenuation function was used to calculate the inundation potential layer of the whole image. Then, the Gaussian kernel was used to weight the physical sensing data based on each water depth point, and finally, the submergence probability layer of the whole image was enhanced. In the validation of the results using radar and social media points, accuracies of 88.77% and 75% were obtained by setting up a threshold classification, demonstrating the effectiveness and usefulness of the method. The significance of this study lies in obtaining discrete social media flood points and achieving space-continuous flood inundation probability mapping, providing decision-making support for urban flood diagnosis and mitigation. [ABSTRACT FROM AUTHOR]

Frequent flooding seriously affects people’s safety and economic construction, and assessing the inundation probability can help to strengthen the capacity of emergency management of floods. There are currently two general means of flood sensing: physical and social. Remote sensing data feature high reliability but are often unavailable in disasters caused by persistent heavy rainfall. Social media is characterized by high timeliness and a large data volume but has high redundancy and low reliability. The existing studies have primarily relied on physical sensing data and have not fully exploited the potential of social media data. This paper combines traditional physical sensing data with social media and proposes an integrated physical and social sensing (IPS) method to estimate the probability distribution of flood inundation. Taking the “7·20” Henan rainstorm in 2021 and the study area of Xinxiang, China, as a case study, more than 60,000 messages and 1900 images about this occurrence were acquired from the Weibo platform. Taking filtered water depth points with their geographic location and water depth information as the main input, the inverse distance attenuation function was used to calculate the inundation potential layer of the whole image. Then, the Gaussian kernel was used to weight the physical sensing data based on each water depth point, and finally, the submergence probability layer of the whole image was enhanced. In the validation of the results using radar and social media points, accuracies of 88.77% and 75% were obtained by setting up a threshold classification, demonstrating the effectiveness and usefulness of the method. The significance of this study lies in obtaining discrete social media flood points and achieving space-continuous flood inundation probability mapping, providing decision-making support for urban flood diagnosis and mitigation.

Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for decisively interpreting the real world. However, various limitations hinder physical sensing's effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm leveraging observations from human participants equipped with portable devices and ubiquitous Internet connectivity to perceive the environment. Despite its virtues, social sensing also inherently suffers from a few drawbacks (e.g., inconsistent reliability and uncertain data provenance). Motivated by the complementary strengths of the two sensing modes, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that explores the collective intelligence of humans and machines to reconstruct the "state of the world," both physically and socially. While a good number of interesting SPS applications have been studied, several critical unsolved challenges still exist in SPS. In this paper, we provide a comprehensive survey of SPS, emphasizing its definition, key enablers, state-of-the-art applications, potential research challenges, and roadmap for future work. This paper intends to bridge the knowledge gap of existing sensing-focused survey papers by thoroughly examining the various aspects of SPS crucial for building potent SPS systems. [ABSTRACT FROM AUTHOR]

Post‐pandemic era poses an imperative demand on progressive sensing devices whose performance largely relies on the morphologies and structures of sensing materials. Despite substantial efforts and advances that have been made in sensing materials with different micro/nanoscale dimensionalities, it is still challenging to couple micro/nano platforms with sensing materials together for the precise and scalable production of high‐performance sensors toward practical application scenarios. Owing to noncontact, precise, and high‐efficiency features, laser micro/nanofabrication offers a promising solution to achieve high‐quality micro/nano sensors with novel functionalities in a relatively short time. Herein, this review begins with a glance over the development of micro/nano‐structured sensors and briefly discusses the importance of laser micro/nanostructuring technology for micro/nano‐engineering of the sensors. Next, representative processing methods are elaborated in detail from a laser‐pulse‐type point of view, with potential applications toward chemical, physical, and biological targets based on different sensing mechanisms summarized. Finally, the perspectives on the opportunities and challenges of laser micro/nanostructuring strategies and materials for micro/nanosensors are presented. [ABSTRACT FROM AUTHOR]

• The principle of Whispering Gallery Mode (WGM) is briefly reviewed. • The optical properties of liquid crystal in WGM are introduced. • The types of WGM sensor with different resonators are reviewed. • The sensing applications of biology, biology and physics are introduced. • The potential application of the WGM is prospected. The Whispering Gallery Mode (WGM) operates on principles akin to those of acoustics. It involves an optical micro-cavity with an echo wall structure, where light waves undergo continuous reflection along the micro-cavity boundary, resulting in the formation of a high quality factor (Q) value. This mode finds applications in various fields, including miniature lasers, sensors, light filters, and optical switches. In comparison to alternative laser technologies, those based on the WGM principle, such as liquid crystal (LC) droplet resonators, solid resonators, and all-optical tunable micro-cavity lasers, exhibit superior sensitivity and high Q. Numerous research groups are actively exploring the potential applications of the WGM. This paper aims to provide a comprehensive introduction to WGM concepts. This article aims to introduce the concept of WGM comprehensively. This paper introduces the fundamental parameters of WGM, conducts a theoretical analysis of the electromagnetic field within the micro-cavity, and explores the optical and sensing characteristics of LC. Furthermore, the paper presents various types of WGM lasers, encompassing solid active resonators, solid passive resonators, liquid active resonators, and liquid passive resonators. The final section of the paper offers a concise overview of the application of resonant cavities based on the WGM in biochemical molecular studies, life sciences, and physical sensing. The paper aims to elucidate the principles and applications of WGM, thereby contributing to the advancement of this promising area of research. [ABSTRACT FROM AUTHOR]

Advancements in robotic surgery help to improve the endoluminal diagnosis and treatment with minimally invasive or non-invasive intervention in a precise and safe manner. Miniaturized probe-based sensors can be used to obtain information about endoluminal anatomy, and they can be integrated with medical robots to augment the convenience of robotic operations. The tremendous benefit of having this physiological information during the intervention has led to the development of a variety of in vivo sensing technologies over the past decades. In this paper, we review the probe-based sensing techniques for the in vivo physical and biochemical sensing in China in recent years, especially on in vivo force sensing, temperature sensing, optical coherence tomography/photoacoustic/ultrasound imaging, chemical sensing, and biomarker sensing. [ABSTRACT FROM AUTHOR]

Printed electronics (PEs), as a fast-growing advanced manufacturing technology in recent years, plays an essential role in development of wearable electronic sensors, flexible displays, human-machine interaction, and thin electronics owing to its low cost, high throughput, and the possibility to be fabricated on diverse thin substrates with good flexibility and stretchability. Various PEs have been developed, such as physical sensing devices, electrochemical sensors, wearable supercapacitors and energy harvesters, stretchable electrodes, thin-film transistors (TFTs), and printed transceiving circuits. In this work, recent progress on physical sensing devices and their interface circuits developed by the printing process are reviewed in terms of their functions, printing methods, materials, and performance. The printed physical sensors used for monitoring the basic biometric parameters through physical sensing mechanisms, such as temperature sensors for skin temperature, pressure sensors for human pulse wave, strain sensors for human motions, biopotential electrodes for electrocardiogram signals, and multiple-function physical sensing platforms, have been studied and highlighted with their good designs and advanced materials. The state-of-the-art printed interface circuits for the wearable sensors such as interconnects, TFTs, digital circuits, amplifiers, oscillators, and antennas have been reviewed in terms of their structures and functions from basic to advanced levels. The challenge and suggestions of the printed wearable devices for future developments are discussed and end with a conclusion. [ABSTRACT FROM AUTHOR]

Optomechanical sensing based on the coupling between mechanical motions and optical resonances has attracted huge interest in sensor applications due to its small footprint, high sensitivity, low detection limit, and electromagnetic immunity. Optomechanical sensors that detect from mechanical motion at µm scale to molecular vibration at nm scale have found various applications such as force, inertia, acoustic, chemical, and thermal sensing, etc. In this review, we provide an overview of the recent progress of optomechanical sensors. Three development fields of optomechanical sensors are reviewed, which are passive optomechanical sensors, electro-optomechanical sensing platforms, and molecular vibration sensing schemes. The sensor configurations, applications, and integrations are described. In the end, we also provide our perspectives on the future development directions of optomechanical sensors.

Advancements in robotic surgery help to improve the endoluminal diagnosis and treatment with minimally invasive or non-invasive intervention in a precise and safe manner. Miniaturized probe-based sensors can be used to obtain information about endoluminal anatomy, and they can be integrated with medical robots to augment the convenience of robotic operations. The tremendous benefit of having this physiological information during the intervention has led to the development of a variety of in vivo sensing technologies over the past decades. In this paper, we review the probe-based sensing techniques for the in vivo physical and biochemical sensing in China in recent years, especially on in vivo force sensing, temperature sensing, optical coherence tomography/photoacoustic/ultrasound imaging, chemical sensing, and biomarker sensing.

Optomechanical sensing based on the coupling between mechanical motions and optical resonances has attracted huge interest in sensor applications due to its small footprint, high sensitivity, low detection limit, and electromagnetic immunity. Optomechanical sensors that detect from mechanical motion at µm scale to molecular vibration at nm scale have found various applications such as force, inertia, acoustic, chemical, and thermal sensing, etc. In this review, we provide an overview of the recent progress of optomechanical sensors. Three development fields of optomechanical sensors are reviewed, which are passive optomechanical sensors, electro-optomechanical sensing platforms, and molecular vibration sensing schemes. The sensor configurations, applications, and integrations are described. In the end, we also provide our perspectives on the future development directions of optomechanical sensors. [ABSTRACT FROM AUTHOR]

Soft electronics are garnering significant attention due to their wide-ranging applications in artificial skin, health monitoring, human-machine interaction, artificial intelligence, and the Internet of Things. Various soft physical sensors such as mechanical sensors, temperature sensors, and humidity sensors are the fundamental building blocks for soft electronics. While the fast growth and widespread utilization of electronic devices have elevated life quality, the consequential electromagnetic interference (EMI) and radiation pose potential threats to device precision and human health. Another substantial concern pertains to overheating issues that occur during prolonged operation. Therefore, the design of multifunctional soft electronics exhibiting excellent capabilities in sensing, EMI shielding, and thermal management is of paramount importance. Because of the prominent advantages in chemical stability, electrical and thermal conductivity, and easy functionalization, new carbon materials including carbon nanotubes, graphene and its derivatives, graphdiyne, and sustainable natural-biomass-derived carbon are particularly promising candidates for multifunctional soft electronics. This review summarizes the latest advancements in multifunctional soft electronics based on new carbon materials across a range of performance aspects, mainly focusing on the structure or composite design, and fabrication method on the physical signals monitoring, EMI shielding, and thermal management. Furthermore, the device integration strategies and corresponding intriguing applications are highlighted. Finally, this review presents prospects aimed at overcoming current barriers and advancing the development of state-of-the-art multifunctional soft electronics., (© 2024 Wiley‐VCH GmbH.)

This paper proposes a framework to investigate the influence of physical interactions to sensory information, during robotic palpation. We embed a capacitive tactile sensor on a robotic arm to probe a soft phantom and detect and classify hard inclusions within it. A combination of PCA and K-Means clustering is used to: first, reduce the dimensionality of the spatiotemporal data obtained through the probing of each area in the phantom; second categorize the re-encoded data into a given number of categories. Results show that appropriate probing interactions can be useful in compensating for the quality of the data, or lack thereof. Finally, we test the proposed framework on a palpation scenario where a Support Vector Machine classifier is trained to discriminate amongst different types of hard inclusions. We show the proposed framework is capable of predicting the best-performing motion strategy, as well as the relative classification performance of the SVM classifier, solely based on unsupervised cluster analysis methods. [ABSTRACT FROM AUTHOR]

A simple mechanical exfoliation of bulk graphite can guarantee the formation of a high-quality, stable 2D carbon-based van der Waals allotrope (graphene). Thus, the industrial-level production of graphene has been intensively exploited using various fabrication methods, including cold-temperature, solution-based drop-and-cast process; ultrahigh vacuum chemical vapor deposition; and the role-to-role-type mass production. Given such industrial maturation in preparing high-fidelity graphene, the industry naturally finds its highest motivated applications in the areas of outstanding optical, electronic, and mechanical properties necessary for designing electronic components spanning from bendable/flexible objects such as light-emitting diodes, detectors, and photovoltaics to user-friendly, high-end wearable objects such as physical sensors, biosensors, and energy storage/harvesters. The details of these flexible/wearable applications will be selectively discussed in this paper. [ABSTRACT FROM AUTHOR]

Physical information sensed by various sensors in a cyber-physical system should be collected for further operation. In many applications, data aggregation should take reliability and delay into consideration. To address these problems, a novel Tiered Structure Routing-based Delay-Aware and Reliable Data Aggregation scheme named TSR-DARDA for spherical physical objects is proposed. By dividing the spherical network constructed by dispersed sensor nodes into circular tiers with specifically designed widths and cells, TSTR-DARDA tries to enable as many nodes as possible to transmit data simultaneously. In order to ensure transmission reliability, lost packets are retransmitted. Moreover, to minimize the latency while maintaining reliability for data collection, in-network aggregation and broadcast techniques are adopted to deal with the transmission between data collecting nodes in the outer layer and their parent data collecting nodes in the inner layer. Thus, the optimization problem is transformed to minimize the delay under reliability constraints by controlling the system parameters. To demonstrate the effectiveness of the proposed scheme, we have conducted extensive theoretical analysis and comparisons to evaluate the performance of TSR-DARDA. The analysis and simulations show that TSR-DARDA leads to lower delay with reliability satisfaction. [ABSTRACT FROM AUTHOR]

Passive radio frequency identification (RFID) sensors are attractive in diverse applications where sensor performance is needed at a low cost and when battery-free operation is critical. We developed a general approach for adapting ubiquitous and cost-effective passive 13.56-MHz RFID tags for diverse sensing applications. In developed RFID sensors, the complex impedance of the RFID resonant antenna is measured and correlated to physical, chemical, or biological properties of interest. In contrast to known wireless sensors, developed RFID sensors combine several measured parameters from the resonant sensor antenna with multivariate data analysis and deliver unique capability for multianalyte sensing and rejection of environmental interferences with a single sensor. Theoretical calculations and experiments in an anechoic chamber demonstrate that the developed RFID sensors are immune to common electromagnetic interferences and the sensor/reader system operates within regulated emission levels. Performance of developed RFID sensors is illustrated in measurements of toxic industrial chemicals (TICs) in air with the detection limit (DL) of 80 parts per billion and in a non-invasive monitoring of milk spoilage. Sensors selectivity is demonstrated in the detection of different vapors with individual sensors. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Physical information sensed by various sensors in a cyber-physical system should be collected for further operation. In many applications, data aggregation should take reliability and delay into consideration. To address these problems, a novel Tiered Structure Routing-based Delay-Aware and Reliable Data Aggregation scheme named TSR-DARDA for spherical physical objects is proposed. By dividing the spherical network constructed by dispersed sensor nodes into circular tiers with specifically designed widths and cells, TSTR-DARDA tries to enable as many nodes as possible to transmit data simultaneously. In order to ensure transmission reliability, lost packets are retransmitted. Moreover, to minimize the latency while maintaining reliability for data collection, in-network aggregation and broadcast techniques are adopted to deal with the transmission between data collecting nodes in the outer layer and their parent data collecting nodes in the inner layer. Thus, the optimization problem is transformed to minimize the delay under reliability constraints by controlling the system parameters. To demonstrate the effectiveness of the proposed scheme, we have conducted extensive theoretical analysis and comparisons to evaluate the performance of TSR-DARDA. The analysis and simulations show that TSR-DARDA leads to lower delay with reliability satisfaction.