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Structural damage can affect the long-term operation of equipment. Real-time damage warning for structures can effectively avoid accidents caused by structural damage. In this paper, a real-time warning method of structural plastic damage based on the cointegration theory is proposed. This method calculates the cointegration relationship between the strain signals at different measuring points, and the stability of the strain signal relationships is also evaluated. The problem of inaccurate detection caused by the error of strain measurement and environmental influence can be eliminated by the comprehensive judgment of strain between asymmetrical measuring points. A real-time damage sensing system is developed in this paper. In order to improve the real-time and practicability of the system, this paper proposes and determines the residual warning coefficient by analyzing the proportion of the strain residuals exceeding the residual threshold. The research on this sensing system has certain value for the engineering application of damage monitoring methods. [ABSTRACT FROM AUTHOR]

Hypothermic preservation (HP) is highly desired for the maintenance of the viability of living cell specimens, e.g. rare cells in whole-blood samples or therapeutic cells, in an unfrozen state. However, the extension of the viable preservation time is a challenge because of the multiple injuries suffered by hypothermically preserved cells. Here, based on a dynamic bond crosslinked zwitterionic hydrogel, we established a sensing preservation system that could monitor the levels of reactive oxygen species (ROS) via real-time electronic signals and intelligent control of antioxidant addition, to completely prevent an excess of ROS in the whole-cell specimen. Furthermore, the hydrogel-based system can counter the extracellular-matrix-loss-induced anoikis of living cells. Based on the design aimed at affording protection against two primary HP injuries (i.e. ROS overproduction and anoikis) to cells, this system extended the preservation time of cell specimens under refrigerated conditions to 24 days. After preservation, the use of a mild cell retrieval process guaranteed the activity of the preserved living cells. This work not only possesses the potential to facilitate intelligent cell-based clinical applications, but also paves the way for the preparation of living materials that can host programmed cells with long-term survival. An intelligent system based on a zwitterionic sensing hydrogel is established, which can afford ultra-long hypothermic cell-preservation times of up to 24 days. The system enables the real-time monitoring of ROS overproduction and intelligent antioxidant addition, because of the merging of the smart hydrogel with a computer intelligent detection and control system. Furthermore, the automatic addition of an antioxidant according to the ROS-signal changes produced by the ZBA hydrogel effectively prevented HP lesions, including ROS over-production and ECM loss, in the preserved living cells. Subsequently, the system could also be gently dissociated, to retrieve the preserved cells. This work provides a solution for the real-time monitoring and long-term HP of living specimens, which holds the promise of benefiting cell-based medicine and the development of genetically programmed cell-based living materials. [Display omitted] [ABSTRACT FROM AUTHOR]

Health assessment and preventive maintenance of structures are mandatory to predict injuries and to schedule required interventions, especially in seismic areas. Structural health monitoring aims to provide a robust and effective approach to obtaining valuable information on structural conditions of buildings and civil infrastructures, in conjunction with methodologies for the identification and, sometimes, localization of potential risks. In this paper a low-cost solution for structural health monitoring is proposed, exploiting a customized embedded system for the acquisition and storing of measurement signals. Experimental surveys for the assessment of the sensing node have also been performed. The obtained results confirmed the expected performances, especially in terms of resolution in acceleration and tilt measurement, which are 0.55 mg and 0.020°, respectively. Moreover, we used a dedicated algorithm for the classification of recorded signals in the following three classes: noise floor (being mainly related to intrinsic noise of the sensing system), exogenous sources (not correlated to the dynamic behavior of the structure), and structural responses (the response of the structure to external stimuli, such as seismic events, artificially forced and/or environmental solicitations). The latter is of main interest for the investigation of structures' health, while other signals need to be recognized and filtered out. The algorithm, which has been tested against real data, demonstrates relevant features in performing the above-mentioned classification task. [ABSTRACT FROM AUTHOR]

To the complexity of networks and the diversity of circuits, multicore embedded sensing systems suffer from low accuracy and efficiency in measuring temperature. To improve the measurement accuracy and efficiency of multicore embedded sensing systems, this paper utilised knowledge distillation, model pruning and parameter quantisation to lightweight neural networks. Meanwhile, the lightweight neural network was applied to multicore embedded sensing systems and the layout of multicore embedded sensing systems based on it was analysed from the perspectives of processor layout, storage design and link network, providing a reference and theoretical basis for further application of multicore embedded sensing systems. [ABSTRACT FROM AUTHOR]

Structural damage can affect the long-term operation of equipment. Real-time damage warning for structures can effectively avoid accidents caused by structural damage. In this paper, a real-time warning method of structural plastic damage based on the cointegration theory is proposed. This method calculates the cointegration relationship between the strain signals at different measuring points, and the stability of the strain signal relationships is also evaluated. The problem of inaccurate detection caused by the error of strain measurement and environmental influence can be eliminated by the comprehensive judgment of strain between asymmetrical measuring points. A real-time damage sensing system is developed in this paper. In order to improve the real-time and practicability of the system, this paper proposes and determines the residual warning coefficient by analyzing the proportion of the strain residuals exceeding the residual threshold. The research on this sensing system has certain value for the engineering application of damage monitoring methods.

This study investigated the accuracy of a near-infrared spectroscopic sensing system for predicting milk quality indicators in cow milk. The system determined three major milk quality indicators (milk fat, protein, and lactose), milk urea nitrogen (MUN), and somatic cell count (SCC) of two Holstein cows at the Hokkaido University dairy farm. The results showed excellent accuracy for milk fat and protein contents, while sufficient accuracy was found for lactose, MUN, and SCC. This suggests that the NIR spectroscopic sensing system could be used for online real-time milk quality determination, aiding dairy farmers in effective individual cow management and smart dairy farming. [ABSTRACT FROM AUTHOR]

Time series forecasting in the sensing system aims to predict future values based on historical records that sensors have collected. Previous works, however, usually focus on improving model structure or algorithm for better performance but the perspective of learning proper numeric representations is overlooked. The inappropriate and coarse numeric representations are not expressive enough to capture the intrinsic characteristics of numbers, which will obviously degrade the prediction performance. In this article, we propose Num2vec, an algorithmic framework to learn numeric representations. Specifically, Num2vec lists three main logic characteristics of numbers: arithmetic, direction, and periodicity. By representing numbers into a transition space, Num2vec can translates numbers agilely to different Internet of Things tasks through selecting the corresponding characteristics. According to such a design, Num2vec enjoys flexible numeric representations to fit different Internet of Things time series tasks. Extensive experiments on four real-world datasets show that the approach achieves the best performance when compared with state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Health assessment and preventive maintenance of structures are mandatory to predict injuries and to schedule required interventions, especially in seismic areas. Structural health monitoring aims to provide a robust and effective approach to obtaining valuable information on structural conditions of buildings and civil infrastructures, in conjunction with methodologies for the identification and, sometimes, localization of potential risks. In this paper a low-cost solution for structural health monitoring is proposed, exploiting a customized embedded system for the acquisition and storing of measurement signals. Experimental surveys for the assessment of the sensing node have also been performed. The obtained results confirmed the expected performances, especially in terms of resolution in acceleration and tilt measurement, which are 0.55 mg and 0.020°, respectively. Moreover, we used a dedicated algorithm for the classification of recorded signals in the following three classes: noise floor (being mainly related to intrinsic noise of the sensing system), exogenous sources (not correlated to the dynamic behavior of the structure), and structural responses (the response of the structure to external stimuli, such as seismic events, artificially forced and/or environmental solicitations). The latter is of main interest for the investigation of structures’ health, while other signals need to be recognized and filtered out. The algorithm, which has been tested against real data, demonstrates relevant features in performing the above-mentioned classification task.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In a Wireless Body Area Network (WBAN), for example, patients' health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity, rendering the data transmission slower. Cognitive radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors, which renders the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts. [ABSTRACT FROM AUTHOR]

This paper presents the development of an electromagnetic (EM) tracking system that consists of a pyramid field generator (PFG) of five excitation coils and a sensing coil for location and orientation estimation. The system measures the induced electromotive forces (EMF) of the sensing coil with the change of the magnetic field contributed by the PFG. The proposed modeling distributed magnetic flux (DMF) method that divides the multi-turns sensing coil into multiple layers and concentric circles for the distributed elements is utilized to determine EMF induced in the sensing coils for investigating the effects of translational and angular displacement. The finite element analysis numerically verifies the DMF method and validates the Artificial Neural Network (ANN)-based estimation models. The calibration and ANN-based estimation process are applied to inversely estimate the location and orientation of the sensing coil based on the DMF method and the experimental EM tracking system. The proposed design and a prototype of the EM tracking system have experimentally verified the DMF method and showed the validity of the location and orientation estimation.

This paper presents a distributed current source (DCS) method for modeling the eddy current (EC) fields induced in biological or non-ferrous metallic objects in two-dimensional axisymmetric and three-dimensional Cartesian coordinates. The EC fields induced in the objects, magnetic flux density (MFD) in space, and magnetic flux (MF) of the sensing coils are formulated in state-space representation. The harmonic responses of the eddy current fields and electromotive force (EMF) of the sensing coil are formulated in closed-form solutions. The proposed DCS method is applied to design two eddy current sensing systems. The Bio-Differential Eddy Current (BD-EC) sensor distinguishes biological objects, and the Metal-Coaxial Eddy Current (MC-EC) sensor classifies non-ferrous metallic objects. The simulated EC field and EMF are numerically verified by comparing results with finite element analysis. An example is utilized to illustrate the advantage of the DCS method for calculating the MFD, MF, and EMF contributed from the induced ECD in the objects directly, and the EMF generated from each material. The proposed method, along with a prototype of the BD-EC sensor, has been experimentally evaluated on sweep frequency analysis for detecting meat and bone.

With the rapid development of economy and the improvement of urbanization level, the contradiction between urban spatial expansion and the decrease of cultivated land resources has become a hot spot in the field of geoscience. Based on satellite remote sensing images, the theory and method of urban spatial expansion and land scale prediction are studied systematically in this paper. This paper mainly discusses the technology of urban area boundary extraction based on remote sensing image, the method of area expansion analysis, the simulation method of cellular automata, the theory and method of urban land scale prediction. Methods of obtaining population are in urban built-up areas. First, in terms of making up for the inability of CA model to express spatial data, GIS technology has data preprocessing functions; Second, under environmental conditions, GIS technology has mature visualization functions, so as to make up for the situation that the CA model cannot immediately present and reflect the operation results. According to the characteristics of urban spatial expansion, combined with the research results in the field of urban can at home and abroad, this paper puts forward the construction scheme of urban spatial expansion can model integrated with gist he results show that the can model integrated with GIS can visualize the simulation results, and the simulation results can be analysed by GIS.

This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework. [ABSTRACT FROM AUTHOR]

The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method for autonomous driving perception systems. To begin, we built an autonomous driving simulation scenario using PreScan software, which collects information from a single millimeter wave (MMW) radar and a single camera sensor. The photos are then identified and labeled via the convolutional neural network (CNN). Then, we fused the sensory inputs from a single MMW radar sensor and a single camera sensor in space and time and mapped the MMW radar points onto the camera image to obtain the region of interest (ROI). Lastly, we developed a method to use information from a single MMW radar to aid in diagnosing defects in a single camera sensor. As the simulation results show, for missing row/column pixel failure, the deviation typically falls between 34.11% and 99.84%, with a response time of 0.02 s to 1.6 s; for pixel shift faults, the deviation range is between 0.32% and 9.92%, with a response time of 0 s to 0.16 s; for target color loss, faults have a deviation range of 0.26% to 2.88% and a response time of 0 s to 0.05 s. These results prove the technology is effective in detecting sensor faults and issuing real-time fault alerts, providing a basis for designing and developing simpler and more user-friendly autonomous driving systems. Furthermore, this method illustrates the principles and methods of information fusion between camera and MMW radar sensors, establishing the foundation for creating more complicated autonomous driving systems. [ABSTRACT FROM AUTHOR]

Tower cranes can cover most of the area of a construction site, which brings significant safety risks, including potential collisions with other entities. To address these issues, it is necessary to obtain accurate and real-time information on the orientation and location of tower cranes and hooks. As a non-invasive sensing method, computer vision-based (CVB) technology is widely applied on construction sites for object detection and three-dimensional (3D) localization. However, most existing methods mainly address the localization on the construction ground plane or rely on specific viewpoints and positions. To address these issues, this study proposes a framework for the real-time recognition and localization of tower cranes and hooks using monocular far-field cameras. The framework consists of four steps: far-field camera autocalibration using feature matching and horizon-line detection, deep learning-based segmentation of tower cranes, geometric feature reconstruction of tower cranes, and 3D localization estimation. The pose estimation of tower cranes using monocular far-field cameras with arbitrary views is the main contribution of this paper. To evaluate the proposed framework, a series of comprehensive experiments were conducted on construction sites in different scenarios and compared with ground-truth data obtained by sensors. The experimental results show that the proposed framework achieves high precision in both crane jib orientation estimation and hook position estimation, thereby contributing to the development of safety management and productivity analysis. [ABSTRACT FROM AUTHOR]

The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale. [ABSTRACT FROM AUTHOR]

Beer is one of the most consumed beverages in the world. Advances in instrumental techniques have allowed the analysis and characterization of a large number of beers. However, review studies that outline the methodologies used in beer characterization are scarce. Herein, a systematic review investigating the molecular targets and sensometric techniques in beer characterization was performed following the PRISMA protocol. The study reviewed 270 articles related to beer analysis in order to provide a comprehensive summary of the recent advances in beer analysis, including methods using sensors and sensing systems. The results revealed the use of various techniques that include several technologies, such as nanotechnology and electronics, often combined with scientific data analysis tools. To our knowledge, this study is the first of its kind and provides the reader with a faithful overview of what has been done in the sensor field regarding beer characterization. [ABSTRACT FROM AUTHOR]

Attention deficit and hyperactivity disorder (ADHD) is a mixed behavioral disorder that exhibits symptoms, such as carelessness and hyperactivity–impulsivity. To date, existing ADHD diagnosis methods rely on observations by observers, such as parents and teachers, which limits the ability to reflect objective evaluation. In this study, to overcome this limitation, we proposed a multiple RGB-D sensor system that can objectively measure the amount of action and attention of children playing a robot-led game. In addition, a classifier was developed to classify children into ADHD, ADHD risk, and normal groups using the multilayer perceptron and data obtained through sensors. The effectiveness of the developed system for ADHD screening was verified. In this study, the priority of abnormal behavior indicators designed for ADHD screening was measured, the features with the highest priority were selected using a feature selection method. Eight hundred and twenty-eight children participated and were classified into the ADHD, ADHD risk, and normal groups, and the results were compared with the diagnosis by clinicians. The proposed system achieved sensitivity of 97.06% and 100%, and specificity of 96.42% and 94.68% in the ADHD and ADHD risk groups, respectively. [ABSTRACT FROM AUTHOR]

Tool condition monitoring (TCM) during mechanical cutting is critical for maximising the utilisation of cutting tools and minimising the risk of equipment damage and personnel injury. The demand for highly efficient and sustainable machining in modern industries has led to the development of new processes operating under specific conditions. Real-world datasets obtained under harsh cutting conditions often suffer from intense interference, making the anti-interference capabilities of TCM methods crucial for effective industrial applications. Previous literature reviews on TCM have focused on theoretical methods for monitoring tool wear and breakage. However, reviews of the scientific methodologies and technologies employed in TCM for industrial production are limited. The lack of scientific understanding relevant to the monitoring of cutting tools in industrial production should be addressed urgently. The current data processing, feature dimensionality reduction, and decision-making methods utilised in TCM may not adequately fulfil the real-time and anti-interference demands. The TCM methods also face the challenges of small sample sizes and imbalanced data during real-world dataset processing. Therefore, this study conducts a systematic review of TCM methods to overcome these limitations. First, the theoretical guidelines for the application of TCM methods in industrial production are provided. The sensing system, signal processing, feature dimensionality reduction, and decision-making methods for TCM methods are comprehensively discussed in terms of both their advantages and limitations for applications in industrial production. Considering the effects of real-world datasets with small samples and imbalanced data caused by the harsh environment of a real factory, a systematic presentation is proposed at the data, feature, and decision levels. Finally, the challenges and potential research directions of TCM methods for industrial applications are discussed. A research route for smart factory-oriented machining system management is proposed based on published literature. This review bridges the gap between theoretical research and the industrial application of TCM techniques in industrial production. Prospective research and further development of TCM systems will provide the groundwork for establishing smart factories. [Display omitted] • Principles and techniques of tool condition monitoring are comprehensively reviewed. • Application guidance for tool condition monitoring is provided. • Challenges of small samples and imbalanced data faced by real dataset are addressed. • A research route is proposed for smart-factory-oriented machining system management. [ABSTRACT FROM AUTHOR]

This study investigated the accuracy of a near-infrared spectroscopic sensing system for predicting milk quality indicators in cow milk. The system determined three major milk quality indicators (milk fat, protein, and lactose), milk urea nitrogen (MUN), and somatic cell count (SCC) of two Holstein cows at the Hokkaido University dairy farm. The results showed excellent accuracy for milk fat and protein contents, while sufficient accuracy was found for lactose, MUN, and SCC. This suggests that the NIR spectroscopic sensing system could be used for online real-time milk quality determination, aiding dairy farmers in effective individual cow management and smart dairy farming.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In a Wireless Body Area Network (WBAN), for example, patients’ health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity, rendering the data transmission slower. Cognitive radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors, which renders the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

Preserving energy in households and office buildings is a significant challenge, mainly due to the recent shortage of energy resources, the uprising of the current environmental problems, and the global lack of utilizing energy-saving technologies. Not to mention, within some regions, COVID-19 social distancing measures have led to a temporary transfer of energy demand from commercial and urban centers to residential areas, causing an increased use and higher charges, and in turn, creating economic impacts on customers. Therefore, the marketplace could benefit from developing an Internet of Things ecosystem that monitors energy consumption habits and promptly recommends action to facilitate energy efficiency. This article aims to present the full integration of a proposed energy efficiency framework into the Home-Assistant platform using an edge-based architecture. End users can visualize their consumption patterns as well as ambient environmental data using the Home-Assistant user interface. More notably, explainable energy-saving recommendations are delivered to end users in the form of notifications via the mobile application to facilitate habit change. In this context, to the best of the authors’ knowledge, this is the first attempt to develop and implement an energy-saving recommender system on edge devices. Thus, ensuring better privacy preservation since data are processed locally on the edge, without the need to transmit them to remote servers, as is the case with cloudlet platforms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a dual-fiberoptic Fabry–Perot interferometer (FFPI) sensing system integrated with a low-cost and low-coherence light-emitting diode (LED) as a light source to detect dynamic vibration caused by acoustic waves with a cut-off frequency of 200 Hz. When the acoustic signals are applied, the sensing FFPI on a Styrofoam sheet provides the function of partially transforming the longitudinal energy as the transverse energy generates a phase shift in the sensing FFPI cavity. The light reflected from the sensor is demodulated by the reference FFPI to extract the measurand. The low-power (sub-nW) optical signals are transferred into electrical signals, processed by a designed optical receiver, and recorded for data analysis. [ABSTRACT FROM AUTHOR]

Dexterous tool use is typically characterized by fast and precise motions performed by multiple fingers. One representative task is piano playing, which involves fast performance of a sequence of complex motions with high spatiotemporal precision. However, for several decades, a lack of contactless sensing technologies that are capable of precision measurement of piano key motions has been a bottleneck for unveiling how such an outstanding skill is cultivated. Here, we developed a novel sensing system that can record the vertical position of all piano keys with a time resolution of 1 ms and a spatial resolution of 0.01 mm in a noncontact manner. Using this system, we recorded the piano key motions while 49 pianists played a complex sequence of tones that required both individuated and coordinated finger movements to be performed as fast and accurately as possible. Penalized regression using various feature variables of the key motions identified distinct characteristics of the key-depressing and key-releasing motions in relation to the speed and accuracy of the performance. For the maximum rate of the keystrokes, individual differences across the pianists were associated with the peak key descending velocity, the key depression duration, and key-lift timing. For the timing error of the keystrokes, the interindividual differences were associated with the peak ascending velocity of the key and the inter-strike variability of both the peak key descending velocity and the key depression duration. These results highlight the importance of dexterous control of the vertical motions of the keys for fast and accurate piano performance. [ABSTRACT FROM AUTHOR]

The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method for autonomous driving perception systems. To begin, we built an autonomous driving simulation scenario using PreScan software, which collects information from a single millimeter wave (MMW) radar and a single camera sensor. The photos are then identified and labeled via the convolutional neural network (CNN). Then, we fused the sensory inputs from a single MMW radar sensor and a single camera sensor in space and time and mapped the MMW radar points onto the camera image to obtain the region of interest (ROI). Lastly, we developed a method to use information from a single MMW radar to aid in diagnosing defects in a single camera sensor. As the simulation results show, for missing row/column pixel failure, the deviation typically falls between 34.11% and 99.84%, with a response time of 0.02 s to 1.6 s; for pixel shift faults, the deviation range is between 0.32% and 9.92%, with a response time of 0 s to 0.16 s; for target color loss, faults have a deviation range of 0.26% to 2.88% and a response time of 0 s to 0.05 s. These results prove the technology is effective in detecting sensor faults and issuing real-time fault alerts, providing a basis for designing and developing simpler and more user-friendly autonomous driving systems. Furthermore, this method illustrates the principles and methods of information fusion between camera and MMW radar sensors, establishing the foundation for creating more complicated autonomous driving systems.

Tower cranes can cover most of the area of a construction site, which brings significant safety risks, including potential collisions with other entities. To address these issues, it is necessary to obtain accurate and real-time information on the orientation and location of tower cranes and hooks. As a non-invasive sensing method, computer vision-based (CVB) technology is widely applied on construction sites for object detection and three-dimensional (3D) localization. However, most existing methods mainly address the localization on the construction ground plane or rely on specific viewpoints and positions. To address these issues, this study proposes a framework for the real-time recognition and localization of tower cranes and hooks using monocular far-field cameras. The framework consists of four steps: far-field camera autocalibration using feature matching and horizon-line detection, deep learning-based segmentation of tower cranes, geometric feature reconstruction of tower cranes, and 3D localization estimation. The pose estimation of tower cranes using monocular far-field cameras with arbitrary views is the main contribution of this paper. To evaluate the proposed framework, a series of comprehensive experiments were conducted on construction sites in different scenarios and compared with ground-truth data obtained by sensors. The experimental results show that the proposed framework achieves high precision in both crane jib orientation estimation and hook position estimation, thereby contributing to the development of safety management and productivity analysis.

The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles.

The ability of "target tracking," such as keeping a target object in sight, is crucial for various activities. However, most sensing systems experience a certain degree of delay due to information processing, which challenges accurate target tracking. The long history of studies on animal behavior has revealed several tactics for it, although a systematic understanding of how individual tactics are combined into a strategy has not been reached. This study demonstrates a multifaceted tracking strategy in animals, which mitigates the adverse delay effects with small implementation costs. Using an active-sensing bat to measure their sensing state while chasing natural prey, we found that bats use a tracking strategy by combining multiple echolocation and flight tactics. The three echolocation tactics, namely the predictive control of sensing direction accompanied by adjusting the sensing rate and angular range, produce a direct compensation effect. Simultaneously, the flight tactic, the counter maneuver, assists echolocation by stabilizing the target direction. Our simulation results demonstrate that these combined tactics improve tracking accuracy over a wide range of delay constraints. In addition, a concise rule based on the angular velocity between bats and targets explains how bats control these tactics, suggesting that bats successfully reduce the burden of multitasking management. Our findings reveal the sophisticated strategy in animals' tracking systems and provide insights into understanding and developing efficiently integrated strategies in target tracking across various disciplines. [Display omitted] • Bats concurrently deploy three echolocation tactics and a flight tactic for tracking • These echolocation and flight tactics were combined to improve tracking accuracy • A single kinematic parameter possibly explains the control of these four tactics • We proposed a framework for how bats integrate these tactics Target tracking is an essential ability for various behavioral contexts. Nishiumi et al. reveal that bats use a combination of several echolocation and flight tactics to effectively track agilely fleeing targets under information delay constraints. They also identify the specific roles of each tactic and propose a control scheme for these tactics. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale.

In order to prevent illegal intrusion, theft, and destruction, important places require stable and reliable human intrusion detection technology to maintain security. In this paper, a combined sensing system using anti-jamming random code signals is proposed and demonstrated experimentally to detect the human intruder in the protected area. This sensing system combines the leaky coaxial cable (LCX) sensor and the single-transmitter-double-receivers (STDR) radar sensor. They transmit the orthogonal physical random code signals generated by Boolean chaos as the detection signals. The LCX sensor realizes the early intrusion alarm at the protected area boundary by comparing the correlation traces before and after intrusion. Meanwhile, the STDR radar sensor is used to track the intruder's moving path inside the protected area by correlation ranging and ellipse positioning, as well as recognizing intruder's activities by time-frequency analysis, feature extraction, and support vector machine. The experimental results demonstrate that this combined sensing system not only realizes the early alarm and path tracking for the intruder with the 13 cm positioning accuracy, but also recognizes the intruder's eight activities including squatting, picking up, jumping, waving, walking forward, running forward, walking backward, and running backward with 98.75% average accuracy. Benefiting from the natural randomness and auto-correlation of random code signal, the proposed sensing system is also proved to have a large anti-jamming tolerance of 27.6 dB, which can be used in the complex electromagnetic environment. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to introduce a new concept, named "one4all" in the realm of SCADA (Supervisory Control and Data Acquisition) systems, used by a regional company (particularly a water supplying company) for managing the different views of its users. As a secondary purpose, the paper presents an integration of such an SCADA system with a GIS (Geographical Information System) system. All the regional water supply companies in Romania manage water and wastewater networks, many sensors and actuators, dozens of water pump plants, several water treatment and wastewater plants, tanks and many hydrophores in different parts of their operating range. Due to the wide geographical operating range, an SCADA system needs to be put in place, but the management of such a system in a traditional way is hard to implement, especially when the human resource is low. The methodology presented in this paper, involving adding helper tables and dynamic template windows within an SCADA tool ("one4all" tool), will show how efficiently the human resource can be used. Additionally, the paper shows that companies as described above, can use a single SCADA system that generates different views for all the managed sub regions instead of different systems for every sub region. Implementing only one SCADA system built with the concept "one4all" in mind, and integrating it with a GIS system that is built on the same principle, represents a new approach that will bring value to any organization willing to adopt it. The concept of "one4all", implemented as a software tool for an SCADA system, is a new concept that will help any developer to easily build applications that generate different views for different users based on their permissions and their operating range. Finally, the paper presents some examples of the same concept, implemented in a different vertical (GIS) and programming language, thus presenting that a "one4all" concept is viable and helpful, bringing value to the information technology industry. [ABSTRACT FROM AUTHOR]

As pioneering information technology, the Internet of Things (IoT) targets at building an infrastructure of embedded devices and networks of connected objects, to offer omnipresent ecosystem and interaction across billions of smart devices, sensors, and actuators. The deployment of IoT calls for decentralized power supplies, self-powered sensors, and wireless transmission technologies, which have brought both opportunities and challenges to the existing solutions, especially when the network scales up. The Triboelectric Nanogenerators (TENGs), recently developed for mechanical energy harvesting and mechanical-to-electrical signal conversion, have the natural properties of energy and information, which have demonstrated high potentials in various applications of IoT. This context provides a comprehensive review of TENG enabled IoT and discusses the most popular and significant divisions. Firstly, the basic principle of TENG is re-examined in this article. Subsequently, a comprehensive and detailed review is given to the concept of IoT, followed by the scientific development of the TENG enabled IoT. Finally, the future of this evolving area is addressed.

Continuous monitoring of the diameter and rings of woody plants is required to evaluate agroforestry products such as timber. Manual measurement is still widely used to monitor tree growth but it is time-consuming. In addition, advanced equipment and software are available but they require technical skills and are costly for smallholders. However, we have developed a rapid and low-cost sensing system for estimating the diameters of woody plants. The apparatus includes a sensor and Internet of things (IoT) technology, which can be used under any network conditions. The apparatus was tested with various trees and the results were compared with those obtained by manual measurement. The findings using the newly developed system demonstrate a strong correlation (R2 = 0.99) with the manually performed measurements. This rapid and low-cost diameter sensing system will be useful in the expansion of agroforestry sensing networks and beneficial to smallholders. Hence, the system will assist in informing foresters of potential problems in timely manner.

Attention deficit and hyperactivity disorder (ADHD) is a mixed behavioral disorder that exhibits symptoms, such as carelessness and hyperactivity–impulsivity. To date, existing ADHD diagnosis methods rely on observations by observers, such as parents and teachers, which limits the ability to reflect objective evaluation. In this study, to overcome this limitation, we proposed a multiple RGB-D sensor system that can objectively measure the amount of action and attention of children playing a robot-led game. In addition, a classifier was developed to classify children into ADHD, ADHD risk, and normal groups using the multilayer perceptron and data obtained through sensors. The effectiveness of the developed system for ADHD screening was verified. In this study, the priority of abnormal behavior indicators designed for ADHD screening was measured, the features with the highest priority were selected using a feature selection method. Eight hundred and twenty-eight children participated and were classified into the ADHD, ADHD risk, and normal groups, and the results were compared with the diagnosis by clinicians. The proposed system achieved sensitivity of 97.06% and 100%, and specificity of 96.42% and 94.68% in the ADHD and ADHD risk groups, respectively.

Touch Craft Ltd completed a feasibility study to understand the market position, technologies and user requirement for a smart sock sensing system designed to help monitor foot problems for people living with diabetes. The proposed system would involve a non-invasive, low-cost approach to gathering and measuring skin and foot data considering parameters such as pressure, temperature, and activity levels. The smart sock sensing system would enable both clinicians and patients to have a broader view of foot health and assist in improving the quality of life for patients and their carers.

Beer is one of the most consumed beverages in the world. Advances in instrumental techniques have allowed the analysis and characterization of a large number of beers. However, review studies that outline the methodologies used in beer characterization are scarce. Herein, a systematic review investigating the molecular targets and sensometric techniques in beer characterization was performed following the PRISMA protocol. The study reviewed 270 articles related to beer analysis in order to provide a comprehensive summary of the recent advances in beer analysis, including methods using sensors and sensing systems. The results revealed the use of various techniques that include several technologies, such as nanotechnology and electronics, often combined with scientific data analysis tools. To our knowledge, this study is the first of its kind and provides the reader with a faithful overview of what has been done in the sensor field regarding beer characterization.

Background: The continuous growth of the older adult population will have implications for the organization of health and social care. Potentially, in-home monitoring unobtrusive sensing systems (USSs) can be used to support formal or informal caregivers of older adults, as they can monitor deviant physical and physiological behavior changes. Most existing USSs are not specific to older adult care. Hence, to facilitate the implementation of existing USSs in older adult care, it is important to know which USSs would be more suitable for older adults. Objective: This scoping review aims to examine the literature to identify current USSs for monitoring human activities and behaviors and assess their implementation readiness for older adult care. Methods: We conducted a structured search in the Scopus, Web of Science, and ACM Digital Library databases. Predefined inclusion criteria included studies on unobtrusive sensor-based technology; experimental in nature; aimed at monitoring human social, emotional, physical, and physiological behavior; having the potential to be scalable in in-home care; and having at least 5 adults as participants. Using these criteria, we screened studies by title, abstract, and full text. A deductive thematic analysis based on the Proctor implementation framework along with an additional outcome of external validity was applied to the included studies to identify the factors contributing to successful implementation. Finally, the identified factors were used to report the implementation readiness of the included studies for older adult care. Results: In this review, 52 studies were included. Deductive analysis using the implementation framework by Proctor resulted in six factors that can contribute to the successful implementation of USSs in older adult care: study settings, age of participants, activities monitored, sensor setup, sensing technology used, and usefulness of USSs. These factors were associated with the implementation outcomes as follows: study settings and age of participants contributed to external validity, sensor setup contributed to acceptability, usefulness of USSs contributed to adoption, activities monitored contributed to appropriateness, and sensing technology used contributed to implementation cost. Furthermore, the implementation assessment of the included 52 studies showed that none of the studies addressed all the identified factors. This assessment was useful in highlighting studies that have addressed multiple factors; thus, these studies represent a step ahead in the implementation process. Conclusions: This review is the first to scope state-of-the-art USSs suitable for older adult care. Although the included 52 USS studies fulfilled the basic criteria to be suitable for older adult care, systems leveraging radio frequency technology in a no-contact sensor setup for monitoring life risk or health wellness activities are more suitable for older adult care. Finally, this review has extended the discussion about unobtrusiveness as a property of systems that cannot be measured in binary because it varies greatly with user perception and context. [ABSTRACT FROM AUTHOR]