Search

We are investigating sensor applications of thin‐film devices, such as, visible‐light sensor, infrared‐light sensor, temperature sensor, and magnetic‐field sensor. Particular in this presentation, it is found that the magnetic‐filed sensor can detect the spatial direction of the magnetic field. [ABSTRACT FROM AUTHOR]

We exploited the stability of Donor‐Acceptor polymer on the cylindrical nanopores structure to realize the sensors with a high sensitivity and stability. Then, we propose a double active layer (DL) strategy that exploits an ultra‐thin layer of Poly(3‐hexylthiophene‐2,5‐diyl) (P3HT) to serve as a work function buffer to enhance the operational current. The DL sensor exhibits a sustainable enhanced operational current of microampere level and a stable sensing response even with the presence of P3HT layer. This effect is carefully examined with different aspects, including vertical composition profile of DL configuration, lifetime testing on different sensing layer, morphological analysis, and the versatility of the DL strategy. Finally, we utilize the DL sensor to conduct a tracing of BA concentration in two HD patients before and after HD, and correlate it with the blood urea nitrogen (BUN) levels. A good correlation coefficient of 0.96 is achieved. [ABSTRACT FROM AUTHOR]

SUMMARY This paper is concerned with the distributed [ABSTRACT FROM AUTHOR]

Traditional methods for object detection typically necessitate a substantial amount of training data, and creating high-quality training data is time-consuming. We propose a novel Few-Shot Object Detection network (FSODv2) in this paper that aims to detect objects from previously unseen categories using only a few annotated examples. Attention RPN, Multi-Relation Detector, and Contrastive Training strategy are central to our method (Fan et al., in: CVPR, 2020), which exploit similarity between few shot support set and query set to detect novel objects while suppressing false detection in the background. We also contribute a new dataset, FSOD-1k, which contains 1000 categories of various objects with high-quality annotations to train our network. To the best of our knowledge, this is one of the first datasets designed for few-shot object detection. This paper improves our FSOD model through well-designed model calibration in three areas: (1) we propose an improved FPN with multi-scale support inputs to calibrate the multi-scale support-query feature matching by exploiting multi-scale features from the same support image with different input scales; (2) we introduce a support classification supervision branch to calibrate the support feature supervision, aligning to the query feature training supervision; (3) we propose backbone calibration to preserve prior knowledge while alleviating backbone bias toward base classes by employing classification dataset to help our model calibration procedure, where such dataset has previously only been used for pre-training in other related works. Besides, we propose a Fast Attention RPN to improve evaluation speed and save computational memory during inference. Once trained, our few-shot network can detect objects from previously unseen categories without further training or fine-tuning, resulting in new state-of-the-art performance on different datasets in the few-shot setting. Our method is general in scope and has numerous potential applications. The dataset link is https://github.com/fanq15/Few-Shot-Object-Detection-Dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a predictive power control strategy for the three‐phase, six‐switch Vienna rectifier without grid voltage sensors to reduce the hardware cost and complexity of a high‐power PWM rectifier system. Firstly, an algorithm for calculating the AC‐side voltage in the αβ coordinate system is derived according to the operating principle of the Vienna rectifier, and a voltage observer is constructed by combining a second‐order low‐pass filter to estimate the grid voltage. Secondly, a soft start method is designed to solve the problem that the rectifier is prone to inrush current when it is started. Furthermore, the control method of grid voltage sensorless is combined with predictive power control with good dynamic characteristics and simple parameter settings to form the control strategy proposed in this paper. Finally, simulation analysis and experimental verification are carried out on the proposed control strategy. Simulation and experimental results show that the grid voltage estimation has high accuracy, a good surge current suppression effect, unit power factor operation, low input current harmonic content, and good dynamic and steady‐state performance. Therefore, the correctness and effectiveness of the strategy proposed in this paper are verified. [ABSTRACT FROM AUTHOR]

This paper introduces a Linear Quadratic Gaussian (LQG) controller for a Single-Ended Primary Inductor Converter (SEPIC). The LQG design is based on merging an integral Linear Quadratic Regulator (LQR) with an offline Kalman Filter (commonly referred to as a Linear Quadratic Estimator (LQE)). The robustness of the LQG controller is guaranteed based on the separation principle. This manuscript addresses the need to use observer-based systems for the fourth-order SEPIC, which needs a sensor reduction as an essential requirement. This paper provides a comprehensive, yet systematic, approach to designing the LQG system. The work validates the convergences of the states in an LQG system to an actual value. Furthermore, it compares the performance of an LQG system with a benchmark Type-II industrial controller by means of a simulation of the switched converter model in the Simulink/MATLAB 2023a environment. [ABSTRACT FROM AUTHOR]

The provision of wireless and battery-free power is key to extending the applications of body-worn sensing electronics. This paper investigates the design of an IPT system for a body-worn scenario where the challenges include highly variable coupling, the requirement for the coil/s to be flexible, and close proximity to the body. Variable coupling results in a system that must operate with received powers ranging over orders of magnitude, whilst the use of flexible coils reduces the Q-factor and results in the potential for inductance variation. The human exposure considerations limit both the maximum field strengths that the wearer of a receiver coil might experience and the field strengths that a third party might be exposed to. In this paper, analytical models are used to identify key design variables and to guide the design synthesis of an IPT system for a wrist-worn medical sensor. Practical circuits to drive the transmit coil and to interface the receive coil with the load electronics are described. A prototype system is tested to validate the theoretical analysis, providing power greater than 2 mW to the sensor over a hemispherical region up to 250 mm in radius from the transmit coil. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the research of phenomena noticed during tests of biodegradable carrageenan-based force and pressure sensors. Peculiar voltage characteristics were noticed during the impact tests. Therefore, the sensors' responses to impact were researched more thoroughly, defining time-dependent sensor output signals from calibrated energy impact. The research was performed using experimental methods when a free-falling steel ball impacted the sensor material to create relatively definable impact energy. The sensor's output signal, which is analogue voltage, was registered using an oscilloscope and transmitted to the PC for further analysis. The obtained results showed a very interesting outcome, where the sensor, which was intended to be piezoresistive, demonstrated a combination of behaviour typical for galvanic cells and piezoelectric material. It provides a stable DC output that is sensitive to the applied statical pressure, and in case of a sudden impact, like a hit, it demonstrates piezoelectric behaviour with some particular effects, which are described in the paper as proton transfer in the sensor-sensitive material. Such phenomena and sensor design are a matter of further development and research. [ABSTRACT FROM AUTHOR]

Climbing gyms aim to continuously improve their offerings and make the best use of their infrastructure to provide a unique experience for their clients, the climbers. One approach to achieve this goal is to track and analyze climbing sessions from the beginning of the ascent until the climber's descent. Detecting the climber's descent is crucial because it indicates when the ascent has ended. This paper discusses an approach that preserves climber privacy (e.g., not using cameras) while considering the convenience of climbers and the costs to the gyms. To this aim, a hardware prototype has been developed to collect data using accelerometer sensors attached to a piece of climbing equipment mounted on the wall, called a quickdraw, which connects the climbing rope to the bolt anchors. The sensors are configured to be energy-efficient, making them practical in terms of expenses and time required for replacement when used in large quantities in a climbing gym. This paper describes the hardware specifications, studies data measured by the sensors in ultra-low power mode, detects sensors' orientation patterns during descent on different routes, and develops a supervised approach to identify lowering. Additionally, the study emphasizes the benefits of multidisciplinary feature engineering, combining domain-specific knowledge with machine learning to enhance performance and simplify implementation. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the problem of sensor fault estimation for linear systems in the presence of unknown inputs in both input and output channels. Relying on matrix equation theory, the unknown inputs are first removed from the output channel. Second, an augmented descriptor system is constructed by treating both system state and sensor fault as (pseudo)‐state variables. Third, the augmented descriptor system is transformed into a regular one, followed by the elimination of the unknown input from the state equation. Finally, a reduced‐order prescribed‐time sensor fault estimator is obtained by using periodic delayed observers. This matrix equation based approach is then complemented by an alternative one, which relies on successive transformations of state, input and outputs. This approach sheds light on the adopted solvability conditions throughout the paper and on the role of different directions in the output space in handling unknown inputs, sensor faults and the core state estimation problem. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed methodologies. [ABSTRACT FROM AUTHOR]

Fingerprint and iris traits are used in sensitive applications and so, spoofing them can impose a serious security threat as well as financial damages. Spoofing is a process of breaking biometric security using artificial biometric traits. This spoofing can be avoided by detecting the liveness of the biometric traits. Hence, liveness detection techniques have become an active research area. However, liveness detection techniques are also prone to attack because of advanced spoofing materials. Hence, they are subjected to further development to face futuristic spoofing and compromising real biometric traits. To aid the development, this paper technically and informatically reviews the state-of-the-art liveness detection techniques in the last decade. Firstly, the paper reviews the processing strata, adopted features and detectors in the existing liveness detection techniques. Secondly, the paper presents the benchmark datasets, their characteristics, availability and accessibility, along with the potential spoofing materials that have been reported in the literature under study. Thirdly, the survey reports the performance of the techniques on the benchmark datasets. Eventually, this paper summarizes the findings, gaps and limitations to facilitate strengthening of liveness detection techniques. This paper further reports that the Fingerprint Liveness Detection (FLD) techniques such as Slim-ResCNN, JLW and Jung CNN have achieved a better accuracy of 94.30%, 98.61% and 97.99%, respectively on LivDet19 datasets. It has been observed that CNN-based architectures have outperformed in significant number of FLD datasets. In contrast, Support Vector Machine (SVM) with appropriate shallow and deep features has achieved equivalent performance against deep classifiers on detecting iris spoofs from Iris Liveness Detection (ILD) datasets. [ABSTRACT FROM AUTHOR]

In complex environments a single visible image is not good enough to perceive the environment, this paper proposes a novel dual-stream real-time detector designed for target detection in extreme environments such as nighttime and fog, which is able to efficiently utilise both visible and infrared images to achieve Fast All-Weatherenvironment sensing (FAWDet). Firstly, in order to allow the network to process information from different modalities simultaneously, this paper expands the state-of-the-art end-to-end detector YOLOv8, the backbone is expanded in parallel as a dual stream. Then, for purpose of avoid information loss in the process of network deepening, a cross-modal feature enhancement module is designed in this study, which enhances each modal feature by cross-modal attention mechanisms, thus effectively avoiding information loss and improving the detection capability of small targets. In addition, for the significant differences between modal features, this paper proposes a three-stage fusion strategy to optimise the feature integration through the fusion of spatial, channel and overall dimensions. It is worth mentioning that the cross-modal feature fusion module adopts an end-to-end training approach. Extensive experiments on two datasets validate that the proposed method achieves state-of-the-art performance in detecting small targets. The cross-modal real-time detector in this study not only demonstrates excellent stability and robust detection performance, but also provides a new solution for target detection techniques in extreme environments. [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the hazardous effects of various volatile organic compounds (VOCs) on human health and its detection methods have been discussed. This paper also presents the comprehensive study on various VOC sensing mechanisms, their advantages, limitations and key design challenges. It is also reported that acoustic wave sensors are more suitable for VOC detection because of its various advantages as compared to other techniques such as, extended lifetime, miniaturization, high sensitivity, less/zero secondary pollution, low power consumption, lower detection limit and low-cost. The main focus of this paper is on bulk acoustic wave (BAW) devices for VOC detection. The development of film bulk acoustic wave resonators (FBARs) in various aspects such as, structural classification, operating modes, selection criteria of materials (electrode, piezoelectric and sensing layer) and their deposition methods, fabrication process flow and applications have been discussed in this detailed review. Various performance enhancement methods for efficient VOC detection using FBARs have been described in detail to design, model, analyze and optimize a FBAR sensors for the detection of various hazardous VOCs with enriched sensitivity and selectivity. [ABSTRACT FROM AUTHOR]

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle (USV) perception, this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection. Firstly, the visual recognition component employs an improved YOLOv7 algorithm based on a self-built dataset for the detection of water surface targets. This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure, addressing the problem of excessive redundant information during feature extraction in the original YOLOv7 network model. Simultaneously, this modification simplifies the computational burden of the detector, reduces inference time, and maintains accuracy. Secondly, to tackle the issue of sample imbalance in the self-built dataset, slide loss function is introduced. Finally, this paper replaces the original Complete Intersection over Union (CIoU) loss function with the Minimum Point Distance Intersection over Union (MPDIoU) loss function in the YOLOv7 algorithm, which accelerates model learning and enhances robustness. To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms, this paper further adopts the fusion of LiDAR and camera data, projecting the three-dimensional point-cloud data from LiDAR onto a two-dimensional pixel plane. This significantly reduces the rate of missed detections for water surface targets. [ABSTRACT FROM AUTHOR]

A lightweight forest fire detection model based on YOLOv8 is proposed in this paper in response to the problems existing in traditional sensors for forest fire detection. The performance of traditional sensors is easily constrained by hardware computing power, and their adaptability in different environments needs improvement. To balance the accuracy and speed of fire detection, the GhostNetV2 lightweight network is adopted to replace the backbone network for feature extraction of YOLOv8. The Ghost module is utilized to replace traditional convolution operations, conducting feature extraction independently in different dimensional channels, significantly reducing the complexity of the model while maintaining excellent performance. Additionally, an improved CPDCA channel priority attention mechanism is proposed, which extracts spatial features through dilated convolution, thereby reducing computational overhead and enabling the model to focus more on fire targets, achieving more accurate detection. In response to the problem of small targets in fire detection, the Inner IoU loss function is introduced. By adjusting the size of the auxiliary bounding boxes, this function effectively enhances the convergence effect of small target detection, further reducing missed detections, and improving overall detection accuracy. Experimental results indicate that, compared with traditional methods, the algorithm proposed in this paper significantly improves the average precision and FPS of fire detection while maintaining a smaller model size. Through experimental analysis, compared with YOLOv3-tiny, the average precision increased by 5.9% and the frame rate reached 285.3 FPS when the model size was only 4.9 M; compared with Shufflenet, the average precision increased by 2.9%, and the inference speed tripled. Additionally, the algorithm effectively addresses false positives, such as cloud and reflective light, further enhancing the detection of small targets and reducing missed detections. [ABSTRACT FROM AUTHOR]

This study delves into the opportunities and challenges associated with the deployment of AI tools in the education sector. It systematically explores the potential benefits and risks inherent in utilizing these tools while specifically addressing the complexities of identifying and preventing academic dishonesty. Recognizing the ethical dimensions, the paper further outlines strategies that educational institutions can adopt to ensure the ethical and responsible use of AI tools. Emphasizing a proactive stance, the paper suggests that by implementing these strategies, schools can harness the benefits of AI tools while mitigating the risks associated with potential misuse. As the adoption of AI tools in education continues to expand, all stakeholders must stay abreast of the latest developments in the field. This knowledge equips educators to navigate the opportunities and challenges posed by AI tools, fostering a learning environment that is both secure and conducive to empowering students to realize their full potential. [ABSTRACT FROM AUTHOR]

Refiners are used in the pulp and paper industry to separate wood chips into individual fibres and to develop the morphology of fibres to be suitable for the type and grade of paper to be produced. Within a refiner are discs, at least one of which rotates at high speed and all of which are lined with radial patterns of bars on their opposed surfaces. A mixture of chips and water is accelerated radially through the space between the discs. Compressive and shear forces are applied to chips and fibres as the bars on the opposed discs cross each other. Experiments have shown that certain process variables can greatly affect the forces applied to chips or fibres when bars cross. These findings suggest that the contact mechanics of bar-crossings are a significant factor in the development of fibre properties. To investigate these contact mechanics in operating refiners, a piezoelectric-based sensor has been developed to measure the forces applied to the chips and fibres by the bars. The sensor replaces a small section of a refiner bar and is sensitive to both axial and tangential forces. The current paper describes the evolution of the design of this sensor from a proof-of-concept prototype for a laboratory-scale refiner to the latest generation sensor for a mill-scale refiner. Performance during the most recent mill trials is presented. [ABSTRACT FROM AUTHOR]

The article presents information on the 2005 Communications Society and Information Theory Society Joint Paper Award, which is given annually to an outstanding paper that was published in any of the two societies' publications during the preceding year. This year the award was given to researchers Nihar Jindal, Sriram Vishwanath, and Andrea Goldsmith for their paper "On the Duality of Gaussian Multiple-Access and Broadcast Channels," which appeared in the 2004 issue of the journal "IEEE Transaction on Information Theory." Jindal has done his B.S. in electrical engineering and computer science from the University of California in Berkeley, in 1999. His research work is in the field of the applications of communication and information theory to wireless communication, with a particular focus on multiple-antenna multi-user channels, dynamic resource allocation, sensor and ad hoc networks. Vishwanath's is currently working as an assistant professor in the Department of Electrical and Computer Engineering at the University of Texas in Austin. Goldsmith works as an assistant professor of electric engineering at Stanford University. She has done research in the field of wireless communication, information theory and adaptive resource allocation in wireless networks.

In this paper, various integration schemes for in-cell capacitive touch panels will be reviewed. Continuous efforts have been made to eliminate the interference between display and touch sensor structure. A in-cell LCD module with self-capacitance touch sensor integrated will be presented. [ABSTRACT FROM AUTHOR]

This study presents the design, simulation, implementation, and experimental characterization of a paperbased perforated disposable weight sensor system with a double piezoresistive layer. The demonstrated system is designed to achieve highly sensitive weight sensing operations with low-cost materials. For that purpose, the main fabrication material of the proposed disposable sensor is selected as a 289-µm-thick Strathmore 400 series Bristol paper. Approximately 48-µm-thick piezoresistive graphite paste is coated onto both sides of the paper-based cantilever beam with the aim of acquiring more sensitive weight-sensing capability. Additionally, the proposed paper-based structure has rows of closely spaced perforations at prespecified locations to facilitate the bending of the cantilever beam and to further increase the sensitivity of the system. A peripheral electronic read-out circuitry is developed and integrated into the system. It is experimentally demonstrated that the proposed weight-sensing system can measure miniature weights ranging to 2 g with a resolution of 20 mg. The implemented sensor has a sensitivity of 17.13 mV/mN or 168.01 mV/g. [ABSTRACT FROM AUTHOR]

Immunoassay has the advantages of high sensitivity, high specificity, and simple operation, and has been widely used in the detection of mycotoxins. For several years, time-resolved fluorescence immunochromatography (TRFIA) paper-based sensors have attracted much attention as a simple and low-cost field detection technology. However, a traditional TRFIA paper-based sensor is based on antibody labeling, which cannot easily meet the current detection requirements. A second antibody labeling method was used to amplify the fluorescence signal and improve the detection sensitivity. Polystyrene fluorescent microspheres were combined with sheep anti-mouse IgG to prepare fluorescent probes (Eu-IgGs). After the probe fully reacted with the antibody (Eu-IgGs-Abs) in the sample cell, it was deployed on the paper-based sensor using chromatography. Eu-IgGs-Abs that were not bound to the target were captured on the T-line, while those that were bound were captured on the C-line. The paper-based sensor reflected the corresponding fluorescence intensity change. Because a single molecule of the deoxynivalenol antibody could bind to multiple Eu-IgGs, this method could amplify the fluorescence signal intensity on the unit antibody and improve the detection sensitivity. The working standard curve of the sensor was established under the optimum working conditions. It showed the lower limit of detection and higher recovery rate when it was applied to actual samples and compared with other methods. This sensor has the advantages of high sensitivity, good accuracy, and good specificity, saving the amount of antibody consumed and being suitable for rapid field detection of deoxynivalenol. [ABSTRACT FROM AUTHOR]

PD measurements in dielectric liquids have been normally performed in the past only using conventional narrow bandwidth detectors. The more recent use of ultra-wide bandwidth instruments has permitted to support once again the thesis that the PDs in oils are the consequence of the streamer (vapor phase) formation and its development. In the paper simultaneous PD measurements in insulating liquids using both narrow and ultra-wide bandwidth detectors are presented. Whilst PD detection with narrow bandwidth instruments gives only integrated signals, the use of ultra-wide bandwidth techniques permits to have much more precise information on PD phenomena, which appear to be in the form of pulses (bursts) of ascending magnitude as the bubble within the liquid expands in volume. As reported in the paper, the use of the ultra-wide bandwidth instruments permits also to distinguish the PD pulses occurring in regular voids of oil-paper insulations and PD pulses formed directly in the liquids as consequence of the streamer development. Also in the paper the meaning of the PDIV in oils is discussed, showing its dependence with the definition used, the unpredictable level of the background noise as well as the randomness of the PD initiation process. [ABSTRACT FROM AUTHOR]

In this paper, the feasibility of inkjet printing of circuit and microwave structures on paper-based substrates is investigated for the first time in the implementation of a complete low-cost wireless platform for sensors. First, the system-level design of the module including the amplifier characterization were carried out to ensure optimum performance of the sensor modules in the UHF bands used in RF identification communication. These results were then used to design two different antenna structures, which are printed on paper along with their respective circuit layouts using inket-printing technology. Different techniques were investigated for the assembly of circuit components on the silver printed layouts. Finally, wireless link measurements on the assembled prototypes verified the good performance on the wireless and sensing sides. [ABSTRACT FROM AUTHOR]

Silicon carbide has attracted considerable attention in recent years as a potential material for sensor devices. This paper reviews the current status of SiC technology for a wide range of sensor applications. It is shown that SiC MEMs devices are well-established with operational devices demonstrated at high temperatures (up to 500 °C) for the sensing of motion, acceleration and gas flow. SiC sensors devices using electrical properties as the sensing mechanism have also been demonstrated principally for gas composition and radiation detection and have wide potential use in scientific, medical and combustion monitoring applications. [ABSTRACT FROM AUTHOR]

Sheet tension control is crucial in the paper winding process in order to get properly formed shipping roll(s) produced in the winder. Typically, the tension loop uses load cells as tension sensors, which are expensive and require frequent maintenance and calibration. This paper develops two new tension estimators for a two-drum electric-braking-generator production winder, based on unwinding and rewinding variables, respectively. The estimators consider all the essential dynamic, friction, and inertial variation effects. Both estimators are evaluated in typical trapezoidal winder operating cycles. Multiple sensitivity analyses show that the proposed estimators are robust to normal parameter variations, and give stable and precise sheet tension control without the use of load cell tension loops. [ABSTRACT FROM AUTHOR]

Due to the molecular wire effect, the fluorescent conjugated polymer (FCP) is always one of the focus of the sensor field. However, the complicated synthesis of FCP usually hinders their further practical application. Meanwhile, the multifunctional fluorescence sensor, which can distinguish multi-analyte only by one sensing material, presents high potential prospect in the real application. Here, a relatively simple benzimidazole-based FCP P1 via Suzuki polymerization was synthesized and further used as a multifunctional fluorescence sensor: P1 can not only efficiently detect Fe3+ in solution with a limit of detection (LOD) of 60 nM, but also monitor 2, 4-dinitrotoluene (DNT) vapor with a low LOD of 3.3 ppb. Furthermore, the simple P1 filter paper sensor was prepared and enable visual detection of Fe3+ and DNT with high performances of portability, flexibility and practicability. • A polymer P1 was synthesized and applied in the detection of DNT and Fe3+. • P1 could realize efficient, portable and multifunctional detection of DNT and Fe3+. • The simple P1 filter paper was prepared and enable visual detection of Fe3+ and DNT. [ABSTRACT FROM AUTHOR]

Temporal action detection is an important and fundamental video understanding task that aims to locate the temporal regions where human actions or events may occur and to identify the classes of actions in untrimmed videos. The main challenge of temporal action detection is that videos are usually of different durations and untrimmed. Although existing methods have achieved better results in recent years, there are still some challenges, such as a lack of full utilisation of video context features, insufficient accuracy of generated action boundaries and failure to consider the relationship between proposals. To address the above issues, this paper proposes a Multi-branch Boundary Generation Network (MBGNet) with temporal context aggregation. It improves the performance of temporal action proposal generation by exploiting rich temporal context features and complementary boundary generators.First, we propose a multi-path temporal context feature aggregation (MTCA) module to exploit "local and global" contextual temporal features for the generation of temporal action proposals. Second, in order to generate accurate action boundaries, we design a multi-branch temporal boundary detector (MBG) to optimise the prediction results by exploiting the complementary relationship between the two boundary detectors.In addition, to accurately predict the confidence of densely distributed proposals, we design a proposal relation-aware module (PRAM) that exploits global correlation for proposal relationship modelling. Experiments on the popular datasets ActivityNet1.3, THUMOS14, and HACS demonstrate the effectiveness of the method proposed in this paper on the task of temporal action proposal generation, which can generate action proposals with high precision and recall. Moreover, combining with existing action classifiers can also achieve better performance in temporal action detection.These results demonstrate the effectiveness of the method in this paper in improving the accuracy of temporal action proposal generation and detection. [ABSTRACT FROM AUTHOR]

Tunable piezoresistive sensors based on a drawing on paper with different grades of pencils are proposed. While an external mechanical strain is applied to U-shape resistors prepared by pencil drawings on paper, an increase in the gauge factor with an increase in the graphite resistance is found. The result is attributed to the tunneling effect between neighboring graphite during the drawing process, further suggesting that different-grade pencil drawings can tune the piezoresistive properties. Through many cycles of repeatedly applied and released force, the output voltage for an HB pencil was found to be more sensitive than that for a 9B pencil. [ABSTRACT FROM AUTHOR]

• The probe enabled naked-eye detection of NO 2 − in the range of 0.005–9.2 mg L−1. • Primary mechanism of the sensing process was a diazotization reaction. • Low-cost (0.01$/ strip) paper-based strips showed high selectivity for NO 2 −. • The strips retained the structural integrity after interaction with NO 2 −. • Recovery of NO 2 − in real samples was found be satisfactory (>85%). An economically feasible, readily accessible colorimetric probe based on basic fuchsin (BF) was developed to detect NO 2 − in water and wastewater systems. The study is divided into two phases; liquid phase and solid phase (colorimetric strip). Initially, the parameter optimization was carried out in the liquid phase, followed by sensing via colorimetric strips. Naked eye-colorimetric detection of NO 2 − in liquid phase was in the range of 0.92–9.2 mg L−1 whereas for strips detection range was observed as 0.005–9.2 mg L−1. Proper diffusion of BF on the Whatmann filter paper (W) enhanced the sensitivity of colorimetric strip. The investigation also showed high selectivity towards NO 2 − in the presence of several anions and cations. The colorimetric strips prior to and after interaction with NO 2 − were characterized using FT-IR, Raman, UV-DRS, and XRD. Results confirmed that diazotization reaction between secondary amine portion of the BF and NO 2 − was responsible for the specific color change (pink to colorless). The leaching test reported no leachable fraction from the coating, and this was accompanied by the use of Tween-80 as a surface coating agent. The solid colorimetric probe was showed potential selectivity towards NO 2 − over several interfering ions. Furthermore, the applicability of the prepared strip for detecting NO 2 − in real water samples indicated satisfactory performance. Cost analysis confirmed that the synthesized BF@W strip is a low-cost (0.01$/strip) and a portable colorimetric sensor for the detection of aqueous NO 2 −. [ABSTRACT FROM AUTHOR]

Toxic and nontoxic volatile organic compound (VOC) gases are emitted into the atmosphere from certain solids and liquids as a consequence of wastage and some common daily activities. Inhalation of toxic VOCs has an adverse effect on human health, so it is necessary to monitor their concentration in the atmosphere. In this work, we report on the fabrication of inorganic nanotube (INT)-tungsten disulfide, paper-based graphene–PEDOT:PSS sheet and WS2 nanotube-modified conductive paper-based chemiresistors for VOC gas sensing. The WS2 nanotubes were fabricated by a two-step reaction, that is oxide reduction and sulfurization, carried out at 900 °C. The synthesized nanotubes were characterized by FE-SEM, EDS, XRD, Raman spectroscopy, and TEM. The synthesized nanotubes were 206–267 nm in diameter. The FE-SEM results show the length of the nanotubes to be 4.5–8 µm. The graphene–PEDOT:PSS hybrid conductive paper sheet was fabricated by a continuous coating process. Then, WS2 nanotubes were drop-cast onto conductive paper for fabrication of the chemiresistors. The feasibility and sensitivity of the WS2 nanotube-modified paper-based chemiresistor were tested in four VOC gases at different concentrations at room temperature (RT). Experimental results show the proposed sensor to be more sensitive to butanol gas when the concentration ranges from 50 to 1000 ppm. The limit of detection (LOD) of this chemiresistor for butanol gas was 44.92 ppm. The WS2 nanotube-modified paper-based chemiresistor exhibits good potential as a VOC sensor with the advantages of flexibility, easy fabrication, and low fabrication cost. [ABSTRACT FROM AUTHOR]

With characters of low cost, portability, easy disposal, and high accuracy, as well as bulky reduced laboratory equipment, paper-based sensors are getting increasing attention for reliable indoor/outdoor onsite detection with nonexpert operation. They have become powerful analysis tools in trace detection with ultra-low detection limits and extremely high accuracy, resulting in their great popularity in medical detection, environmental inspection, and other applications. Herein, we summarize and generalize the recently reported paper-based sensors based on their application for mechanics, biomolecules, food safety, and environmental inspection. Based on the biological, physical, and chemical analytes-sensitive electrical or optical signals, extensive detections of a large number of factors such as humidity, pressure, nucleic acid, protein, sugar, biomarkers, metal ions, and organic/inorganic chemical substances have been reported via paper-based sensors. Challenges faced by the current paper-based sensors from the fundamental problems and practical applications are subsequently analyzed; thus, the future directions of paper-based sensors are specified for their rapid handheld testing. [ABSTRACT FROM AUTHOR]

In the last 10 years, paper-based electrochemical biosensors have gathered attention from the scientific community for their unique advantages and sustainability vision. The use of papers in the design the electrochemical biosensors confers to these analytical tools several interesting features such as the management of the solution flow without external equipment, the fabrication of reagent-free devices exploiting the porosity of the paper to store the reagents, and the unprecedented capability to detect the target analyte in gas phase without any sampling system. Furthermore, cost-effective fabrication using printing technologies, including wax and screen-printing, combined with the use of this eco-friendly substrate and the possibility of reducing waste management after measuring by the incineration of the sensor, designate these type of sensors as eco-designed analytical tools. Additionally, the foldability feature of the paper has been recently exploited to design and fabricate 3D multifarious biosensors, which are able to detect different target analytes by using enzymes, antibodies, DNA, molecularly imprinted polymers, and cells as biocomponents. Interestingly, the 3D structure has recently boosted the self-powered paper-based biosensors, opening new frontiers in origami devices. This review aims to give an overview of the current state origami paper-based biosensors, pointing out how the foldability of the paper allows for the development of sensitive, selective, and easy-to-use smart and sustainable analytical devices. [ABSTRACT FROM AUTHOR]

The development of diagnostic tools for measuring a wide spectrum of target analytes, from biomarkers to other biochemical parameters in biological fluids, has experienced a significant growth in the last decades, with a good number of such tools entering the market. Recently, a clear focus has been put on miniaturized wearable devices, which offer powerful capabilities for real-time and continuous analysis of biofluids, mainly sweat, and can be used in athletics, consumer wellness, military, and healthcare applications. Sweat is an attractive biofluid in which different biomarkers could be noninvasively measured to provide rapid information about the physical state of an individual. Wearable devices reported so far often provide discrete (single) measurements of the target analytes, most of them in the form of a yes/no qualitative response. However, quantitative biomarker analysis over certain periods of time is highly demanded for many applications such as the practice of sports or the precise control of the patient status in hospital settings. For this, a feasible combination of fluidic elements and sensor architectures has been sought. In this regard, this paper shows a concise overview of analytical tools based on the use of capillary-driven fluidics taking place on paper or fabric devices integrated with solid-state sensors fabricated by thick film technologies. The main advantages and limitations of the current technologies are pointed out together with the progress towards the development of functional devices. Those approaches reported in the last decade are examined in detail. [ABSTRACT FROM AUTHOR]

Photography-based photometry is a technique developed to perform high throughput UV photometric analysis without instrumental detectors in resource-limited settings. Its principle relies on the illumination of a sample with UV irradiation and then capturing the transmitted irradiation on a photosensitive paper surface. Therefore, the photosensitive surface acts as a detector for the determination of the concentration of analytes in the sample. This work aims to investigate the optimum photosensitive paper coatings for capturing the transmitted UV irradiation. To this end, photosensitive coatings based on silver, iron, and dichromate salts were tested using three assays of pharmaceutical and biochemical interest. The results from both calibrations, using standard solutions and the application in real samples, show that photosensitive coatings based on iron salts provide the best results. Importantly, the detection limits and the linear range of the calibration curves were better than those obtained with standard photometry. Based on these findings, cyanotype green papers, are proposed as optimum detectors for photography-based photometry. This finding simplifies the operation of the technique enabling the fabrication of prototype readers for analytical assays performed in resource limited settings, point-of-need applications or in the field. [ABSTRACT FROM AUTHOR]