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This paper presents a visual compass method utilizing global features, specifically spherical moments. One of the primary challenges faced by photometric methods employing global features is the variation in the image caused by the appearance and disappearance of regions within the camera's field of view as it moves. Additionally, modeling the impact of translational motion on the values of global features poses a significant challenge, as it is dependent on scene depths, particularly for non-planar scenes. To address these issues, this paper combines the utilization of image masks to mitigate abrupt changes in global feature values and the application of neural networks to tackle the modeling challenge posed by translational motion. By employing masks at various locations within the image, multiple estimations of rotation corresponding to the motion of each selected region can be obtained. Our contribution lies in offering a rapid method for implementing numerous masks on the image with real-time inference speed, rendering it suitable for embedded robot applications. Extensive experiments have been conducted on both real-world and synthetic datasets generated using Blender. The results obtained validate the accuracy, robustness, and real-time performance of the proposed method compared to a state-of-the-art method. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available. [ABSTRACT FROM AUTHOR]

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments. [ABSTRACT FROM AUTHOR]

This study investigates the characteristics of a novel origami-based, elastomeric actuator and a soft gripper, which are controlled by hand gestures that are recognized through machine learning algorithms. The lightweight paper–elastomer structure employed in this research exhibits distinct actuation features in four key areas: (1) It requires approximately 20% less pressure for the same bending amplitude compared to pneumatic network actuators (Pneu-Net) of equivalent weight, and even less pressure compared to other actuators with non-linear bending behavior; (2) The control of the device is examined by validating the relationship between pressure and the bending angle, as well as the interaction force and pressure at a fixed bending angle; (3) A soft robotic gripper comprising three actuators is designed. Enveloping and pinch grasping experiments are conducted on various shapes, which demonstrate the gripper's potential in handling a wide range of objects for numerous applications; and (4) A gesture recognition algorithm is developed to control the gripper using electromyogram (EMG) signals from the user's muscles. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for the three-dimensional (3D) image visualization of objects under photon-starved conditions using multiple observations and statistical estimation. To visualize 3D objects under these conditions, photon counting integral imaging was used, which can extract photons from 3D objects using the Poisson random process. However, this process may not reconstruct 3D images under severely photon-starved conditions due to a lack of photons. Therefore, to solve this problem, in this paper, we propose N-observation photon-counting integral imaging with statistical estimation. Since photons are extracted randomly using the Poisson distribution, increasing the samples of photons can improve the accuracy of photon extraction. In addition, by using a statistical estimation method, such as maximum likelihood estimation, 3D images can be reconstructed. To prove our proposed method, we implemented the optical experiment and calculated its performance metrics, which included the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), peak-to-correlation energy (PCE), and the peak sidelobe ratio (PSR). [ABSTRACT FROM AUTHOR]

In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders. [ABSTRACT FROM AUTHOR]

The concept of digital twins is one of the fundamental pillars of Industry 4.0. Digital twin allows the realization of a virtual model of a real system, enhancing the relevant performance (e.g., in terms of production rate, risk prevention, energy saving, and maintenance operation). Current literature presents many contributions pointing out the advantages that may be achieved by the definition of a digital twin of a water supply system. The Reference Architecture Model for Industry 4.0 introduces the concept of the Asset Administration Shell for the digital representation of components within the Industry 4.0 ecosystem. Several proposals are currently available in the literature considering the Asset Administration Shell for the realization of a digital twin of real systems. To the best of the authors' knowledge, at the moment, the adoption of Asset Administration Shell for the digital representation of a water supply system is not present in the current literature. For this reason, the aim of this paper is to present a methodological approach for developing a digital twin of a water supply system using the Asset Administration Shell metamodel. The paper will describe the approach proposed by the author and the relevant model based on Asset Administration Shell, pointing out that its implementation is freely available on the GitHub platform. [ABSTRACT FROM AUTHOR]

The article announces several research papers related to sensing technology that has received the Best Paper Award in 2013 from this very journal including "Metal-Organic Frameworks for Sensing Applications in the Gas Phase," by Sabine Achmann and colleagues, "Miniaturized pH Sensors Based on Zinc Oxide Nanotubes/Nanorods," by Alimujiang Fulati and colleagues and "Applications and Advances in Electronic-Nose Technologies," by Alphus D. Wilson and Manuela Baietto.

In the work reported in this paper, a lost-in-space star pattern identification algorithm for agile spacecraft was studied. Generally, the operation of a star tracker is known to exhibit serious degradation or even failure during fast attitude maneuvers. While tracking methods are widely used solutions to handle the dynamic conditions, they require prior information about the initial orientation. Therefore, the tracking methods may not be adequate for autonomy of attitude and control systems. In this paper a novel autonomous identification method for dynamic conditions is proposed. Additional constraints are taken into account that can significantly decrease the number of stars imaged and the centroid accuracy. A strategy combining two existing classes for star pattern identification is proposed. The new approach is intended to provide a unique way to determine the identity of stars that promises robustness against noise and rapid identification. Moreover, representative algorithms implemented in actual space applications were utilized as counterparts to analyze the performance of the proposed method in various scenarios. Numerical simulations show that the proposed method is not only highly robust against positional noise and false stars, but also guarantees fast run-time, which is appropriate for high-speed applications. [ABSTRACT FROM AUTHOR]

In this paper, we report a simple and inexpensive paper-based microfluidic device for detecting nitrate in water. This device incorporates two recent developments in paper-based technology suitable for nitrate detection and has an optimized microfluidic design. The first technical advancement employed is an innovative fibrous composite material made up of cotton fibers and zinc microparticles that can be incorporated in paper-based devices and results in better nitrate reduction. The second is a detection zone with an immobilized reagent that allows the passage of a larger sample volume. Different acids were tested—citric and phosphoric acids gave better results than hydrochloric acid since this acid evaporates completely without leaving any residue behind on paper. Different microfluidic designs that utilize various fluid control technologies were investigated and a design with a folding detection zone was chosen and optimized to improve the uniformity of the signal produced. The optimized design allowed the device to achieve a limit of detection and quantification of 0.53 ppm and 1.18 ppm, respectively, for nitrate in water. This accounted for more than a 40% improvement on what has been previously realized for the detection of nitrate in water using paper-based technology. [ABSTRACT FROM AUTHOR]

Perfluorinated compounds like perfluorooctanesulfonic acid (PFOS) are synthetic water pollutants and have accumulated in environments for decades, causing a serious global health issue. Conventional assays rely on liquid chromatography and mass spectroscopy that are very expensive and complicated and thus limit the large-scale monitoring of PFOS in wastewater. To achieve low-cost and accurate detection of PFOS, we designed a paper-based sensor with molecularly imprinted polyaniline electrodes that have recognition sites specific to PFOS. The calibration curve of resistivity ratios as a function of PFOS concentrations has a linear range from 1 to 100 ppt with a coefficient of determination of 0.995. The estimated limit of detection is 1.02 ppt. We also investigated attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) and X-ray photoelectron spectroscopy (XPS) spectra of the surface of the polyaniline (PANI) electrodes to propose the potential recognition sites in polyaniline matrix and the detection mechanism. This electrical paper sensor with low cost and excellent sensitivity and selectivity provides the potential for large-scale monitoring of wastewater. [ABSTRACT FROM AUTHOR]

Recently, cellulose has been re-discovered as a smart material that can be used as sensor and actuator materials, which is termed electro-active paper (EAPap). This paper reports recent advances in paper actuators made with cellulose and hybrid materials such as multi-walled carbon nanotubes, conducting polymers and ionic liquids. Two distinct actuator principles in EAPap actuators are demonstrated: piezoelectric effect and ion migration effect in cellulose. Piezoelectricity of cellulose EAPap is quite comparable with other piezoelectric polymers. But, it is biodegradable, biocompatible, mechanically strong and thermally stable. To enhance ion migration effect in the cellulose, polypyrrole conducting polymer and ionic liquids were nanocoated on the cellulose film. This hybrid cellulose EAPap nanocomposite exhibits durable bending actuation in an ambient humidity and temperature condition. Fabrication, characteristics and performance of the cellulose EAPap and its hybrid EAPap materials are illustrated. Also, its possibility for remotely microwave-driven paper actuator is demonstrated. [ABSTRACT FROM AUTHOR]

The electrophoresis of ampholytes such as amino acids on a paper device is difficult because of the variation of pH distribution in time. On the basis of this observation, we propose a paper-based analytical device (PAD) with origami structure. By folding a filter paper, a low operation voltage of 5 V was achieved, where the power was supplied by a 5 V 1.5 A portable power bank through the USB type A receptacle. As a demonstration, we carried out the electrophoretic separation of pI markers (pI 5.5 and 8.7). The separation was achieved within 4 min before the pH distribution on the paper varied. Though the separation distance was small, it could be increased by expanding the origami structure. This result indicates that our proposed PAD is useful for electrophoretic separation on a paper device. [ABSTRACT FROM AUTHOR]

A graphene and poly (3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) modified conductive paper-based electrochemical impedance spectroscopy (EIS) aptasensor has been successfully fabricated by a simple and continuous coating process. A graphene/PEDOT:PSS modified paper electrode forms the nanocomposite providing a conductive and sensitive substrate for further aptamer functionalization of the biosensor. This low-cost paper-based aptasensor exhibits its sensitivity to carcinoembryonic antigens (CEA) in standard buffer solutions and human serum samples in a linear range of 0.77–14 ng·mL−1. The limit of detection (LOD) is found to be 0.45 ng·mL−1 and 1.06 ng·mL−1 for CEA in both samples, separately. This aptamer-based sensing device was also evaluated and received a good correlation with the immunoassay detection method. The proposed paper-based aptasensor has demonstrated its potential as a rapid simple point-of-care analytical platform for early cancer diagnosis in less developed areas where manufacturing facilities, analytical instruments, and trained specialists are limited. [ABSTRACT FROM AUTHOR]

We propose an effective method for the detection and identification of dangerous substances by using the broadband THz pulse. This pulse excites, for example, many vibrational or rotational energy levels of molecules simultaneously. By analyzing the time-dependent spectrum of the THz pulse transmitted through or reflected from a substance, we follow the average response spectrum dynamics. Comparing the absorption and emission spectrum dynamics of a substance under analysis with the corresponding data for a standard substance, one can detect and identify the substance under real conditions taking into account the influence of packing material, water vapor and substance surface. For quality assessment of the standard substance detection in the signal under analysis, we propose time-dependent integral correlation criteria. Restrictions of usually used detection and identification methods, based on a comparison between the absorption frequencies of a substance under analysis and a standard substance, are demonstrated using a physical experiment with paper napkins. [ABSTRACT FROM AUTHOR]

This Special Issue covers a broad range of topics in infrared (IR) vision technology, including novel sensing techniques, advanced signal processing algorithms, and innovative applications. The papers in this Special Issue are as follows: Fractal Texture Enhancement of Simulated Infrared Images Using a CNN-Based Neural Style Transfer Algorithm with a Histogram Matching Technique [[1]]: This paper proposes a novel convolutional neural network (CNN)-based infrared image enhancement method to transform pseudo-realistic regions of simulation-based infrared images into real infrared texture. Dear readers and fellow researchers, I am pleased to introduce this Special Issue of I Sensors i , entitled "Sensing and Processing for Infrared Vision: Methods and Applications". [Extracted from the article]

Surface-enhanced Raman spectroscopy (SERS) technology is an attractive method for the prompt and accurate on-site screening of illicit drugs. As portable Raman systems are available for on-site screening, the readiness of SERS technology for sensing applications is predominantly dependent on the accuracy, stability and cost-effectiveness of the SERS strip. An atmospheric-pressure plasma-assisted chemical deposition process that can deposit an even distribution of nanogold particles in a one-step process has been developed. The process was used to print a nanogold film on a paper-based substrate using a HAuCl4 solution precursor. X-ray photoelectron spectroscopy (XPS) analysis demonstrates that the gold has been fully reduced and that subsequent plasma post-treatment decreases the carbon content of the film. Results for cocaine detection using this substrate were compared with two commercial SERS substrates, one based on nanogold on paper and the currently available best commercial SERS substrate based on an Ag pillar structure. A larger number of bands associated with cocaine was detected using the plasma-printed substrate than the commercial substrates across a range of cocaine concentrations from 1 to 5000 ng/mL. A detection limit as low as 1 ng/mL cocaine with high spatial uniformity was demonstrated with the plasma-printed substrate. It is shown that the plasma-printed substrate can be produced at a much lower cost than the price of the commercial substrate. [ABSTRACT FROM AUTHOR]

This paper proposes a combined strategy of using paper-based competitive immunochromatography and a near field communication (NFC) tag for wireless cotinine determination. The glucose oxidase labeled cotinine antibody specifically binds free cotinine in a sample, whereas the unoccupied antibody attached to BSA-cotinine at the test line on a lateral flow strip. The glucose oxidase on the strip and an assistant pad in the presence of glucose generated H2O2 and imposed the Ag oxidation on the modified electrode. This enabled monitoring of immunoreaction by either electrochemical measurement or wireless detection. Wireless sensing was realized for cotinine in the range of 100–1000 ng/mL (R2 = 0.96) in PBS medium. Undiluted urine samples from non-smokers exhibited an Ag-oxidation rate three times higher than the smoker's urine samples. For 1:8 diluted urine samples (smokers), the proposed paper-based competitive immunochromatography coupled with an enzyme-modified electrode differentiated positive and negative samples and exhibited cotinine discrimination at levels higher than 12 ng/mL. This novel sensing platform can potentially be combined with a smartphone as a reader unit. [ABSTRACT FROM AUTHOR]

We report for the first time a distance-based paper device based on gold/silver core shell nanoparticles (Au@Ag NPs) for a simple, inexpensive, instrument-free, and portable determination of cyanide by the naked eye. Au@Ag NPs immobilized on a paper channel were etched by cyanide ions so that a yellow color band length of Au@Ag NPs is proportional to a decrease in the cyanide concentration. Quantification is achieved by measuring color length, thus eliminating the need to differentiate hues and intensities by the user, and the processing data of each imaging device. Moreover, the paper-based headspace extraction was combined with the distance-based paper device to improve the sensitivity. The enrichment factor was found to be 30-fold and the linearity was found in the range 0.05–1 mg L−1. The naked eye detection limit was 10 μg L−1 where the World Health Organization (WHO) have regulated the maximum level of cyanide in drinking water as 70 μg L−1. Our proposed device also showed no interference from common cations and anions presenting in seawater and waste water including thiocyanate, chloride. Finally, our device has been successfully applied to determine cyanide ions in seawater, drinking water, tap water and wastewater providing satisfactory precision and accuracy. [ABSTRACT FROM AUTHOR]

This paper demonstrates the performance of a potential design of a paper substrate-based flexible antenna for intrabody telemedicine systems in the 2.4 GHz industrial, scientific, and medical radio (ISM) bands. The antenna was fabricated using 0.54 mm thick flexible photo paper and 0.03 mm copper strips as radiating elements. Design and performance analyses of the antenna were performed using Computer Simulation Technology (CST) Microwave Studio software. The antenna performances were investigated based on the reflection coefficient in normal and bent conditions. The total dimensions of the proposed antenna are 40 × 35 × 0.6 mm3. The antenna operates at 2.33–2.53 GHz in the normal condition. More than an 8% fractional bandwidth is expressed by the antenna. Computational analysis was performed at different flexible curvatures by bending the antenna. The minimum fractional bandwidth deviation is 5.04% and the maximum is 24.97%. Moreover, it was mounted on a homogeneous phantom muscle and a four-layer human tissue phantom. Up to a 70% radiation efficiency with a 2 dB gain was achieved by the antenna. Finally, the performance of the antenna with a homogeneous phantom muscle was measured and found reliable for wearable telemedicine applications. [ABSTRACT FROM AUTHOR]

In healthcare, new diagnostic tools that help in the diagnosis, prognosis, and monitoring of diseases rapidly and accurately are in high demand. For in-situ measurement of disease or infection biomarkers, point-of-care devices provide a dramatic speed advantage over conventional techniques, thus aiding clinicians in decision-making. During the last decade, paper-based analytical devices, combining paper substrates and electrochemical detection components, have emerged as important point-of-need diagnostic tools. This review highlights significant works on this topic over the last five years, from 2015 to 2019. The most relevant articles published in 2018 and 2019 are examined in detail, focusing on device fabrication techniques and materials applied to the production of paper fluidic and electrochemical cell architectures as well as on the final device assembly. Two main approaches were identified, that are, on one hand, those ones where the fabrication of the electrochemical cell is done on the paper substrate, where the fluidic structures are also defined, and, on the other hand, the fabrication of those ones where the electrochemical cell and liquid-driving paper component are defined on different substrates and then heterogeneously assembled. The main limitations of the current technologies are outlined and an outlook on the current technology status and future prospects is given. [ABSTRACT FROM AUTHOR]

Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented. [ABSTRACT FROM AUTHOR]

An autonomous place recognition system is essential for scenarios where GPS is useless, such as underground tunnels. However, it is difficult to use existing algorithms to fully utilize the small number of effective features in underground tunnel data, and recognition accuracy is difficult to guarantee. In order to solve this challenge, an efficient point cloud position recognition algorithm, named Dual-Attention Transformer Network (DAT-Net), is proposed in this paper. The algorithm firstly adopts the farthest point downsampling module to eliminate the invalid redundant points in the point cloud data and retain the basic shape of the point cloud, which reduces the size of the point cloud and, at the same time, reduces the influence of the invalid point cloud on the data analysis. After that, this paper proposes the dual-attention Transformer module to facilitate local information exchange by utilizing the multi-head self-attention mechanism. It extracts local descriptors and integrates highly discriminative global descriptors based on global context with the help of a feature fusion layer to obtain a more accurate and robust global feature representation. Experimental results show that the method proposed in this paper achieves an average F 1 score of 0.841 on the SubT-Tunnel dataset and outperforms many existing state-of-the-art algorithms in recognition accuracy and robustness tests. [ABSTRACT FROM AUTHOR]

(1) Background: The ability to recognize identities is an essential component of security. Electrocardiogram (ECG) signals have gained popularity for identity recognition because of their universal, unique, stable, and measurable characteristics. To ensure accurate identification of ECG signals, this paper proposes an approach which involves mixed feature sampling, sparse representation, and recognition. (2) Methods: This paper introduces a new method of identifying individuals through their ECG signals. This technique combines the extraction of fixed ECG features and specific frequency features to improve accuracy in ECG identity recognition. This approach uses the wavelet transform to extract frequency bands which contain personal information features from the ECG signals. These bands are reconstructed, and the single R-peak localization determines the ECG window. The signals are segmented and standardized based on the located windows. A sparse dictionary is created using the standardized ECG signals, and the KSVD (K-Orthogonal Matching Pursuit) algorithm is employed to project ECG target signals into a sparse vector–matrix representation. To extract the final representation of the target signals for identification, the sparse coefficient vectors in the signals are maximally pooled. For recognition, the co-dimensional bundle search method is used in this paper. (3) Results: This paper utilizes the publicly available European ST-T database for our study. Specifically, this paper selects ECG signals from 20, 50 and 70 subjects, each with 30 testing segments. The method proposed in this paper achieved recognition rates of 99.14%, 99.09%, and 99.05%, respectively. (4) Conclusion: The experiments indicate that the method proposed in this paper can accurately capture, represent and identify ECG signals. [ABSTRACT FROM AUTHOR]

Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang–Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang–Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks. [ABSTRACT FROM AUTHOR]

Air pollution is a critical problem in densely populated urban areas, with traffic significantly contributing. To mitigate the adverse effects of air pollution on public health and the environment, there is a growing need for the real-time monitoring and detection of pollution spikes in transportation. This paper presents a novel approach to using Internet of Things (IoT) edge networks for the real-time detection of air pollution peaks in transportation, specifically designed for innovative city applications. The proposed system uses IoT sensors in buses, cabs, and private cars. These sensors are equipped with air quality monitoring capabilities, including the measurement of pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon dioxide (CO2). The sensors continuously collect air quality data and transmit them to edge devices within the transportation infrastructure. The data collected by these sensors are analyzed, and alerts are generated when pollution levels exceed predefined thresholds. By deploying this system within IoT edge networks, transportation authorities can promptly respond to pollution spikes, improving air quality, public health, and environmental sustainability. This paper details the sensor technology, data analysis methods, and the practical implementation of this innovative system, shedding light on its potential for addressing the pressing issue of transportation-related pollution. The proposed IoT edge network for real-time air pollution spike detection in transportation offers significant advantages, including low-latency data processing, scalability, and cost-effectiveness. By leveraging the power of edge computing and IoT technologies, smart cities can proactively monitor and manage air pollution, leading to healthier and more sustainable urban environments. [ABSTRACT FROM AUTHOR]

Sugarcane is an important raw material for sugar and chemical production. However, in recent years, various sugarcane diseases have emerged, severely impacting the national economy. To address the issue of identifying diseases in sugarcane leaf sections, this paper proposes the SE-VIT hybrid network. Unlike traditional methods that directly use models for classification, this paper compares threshold, K-means, and support vector machine (SVM) algorithms for extracting leaf lesions from images. Due to SVM's ability to accurately segment these lesions, it is ultimately selected for the task. The paper introduces the SE attention module into ResNet-18 (CNN), enhancing the learning of inter-channel weights. After the pooling layer, multi-head self-attention (MHSA) is incorporated. Finally, with the inclusion of 2D relative positional encoding, the accuracy is improved by 5.1%, precision by 3.23%, and recall by 5.17%. The SE-VIT hybrid network model achieves an accuracy of 97.26% on the PlantVillage dataset. Additionally, when compared to four existing classical neural network models, SE-VIT demonstrates significantly higher accuracy and precision, reaching 89.57% accuracy. Therefore, the method proposed in this paper can provide technical support for intelligent management of sugarcane plantations and offer insights for addressing plant diseases with limited datasets. [ABSTRACT FROM AUTHOR]

As globalization accelerates, the linguistic diversity and semantic complexity of in-vehicle communication is increasing. In order to meet the needs of different language speakers, this paper proposes an interactive attention-based contrastive learning framework (IABCL) for the field of in-vehicle dialogue, aiming to effectively enhance cross-lingual natural language understanding (NLU). The proposed framework aims to address the challenges of cross-lingual interaction in in-vehicle dialogue systems and provide an effective solution. IABCL is based on a contrastive learning and attention mechanism. First, contrastive learning is applied in the encoder stage. Positive and negative samples are used to allow the model to learn different linguistic expressions of similar meanings. Its main role is to improve the cross-lingual learning ability of the model. Second, the attention mechanism is applied in the decoder stage. By articulating slots and intents with each other, it allows the model to learn the relationship between the two, thus improving the ability of natural language understanding in languages of the same language family. In addition, this paper constructed a multilingual in-vehicle dialogue (MIvD) dataset for experimental evaluation to demonstrate the effectiveness and accuracy of the IABCL framework in cross-lingual dialogue. With the framework studied in this paper, IABCL improves by 2.42% in intent, 1.43% in slot, and 2.67% in overall when compared with the latest model. [ABSTRACT FROM AUTHOR]

Accurately detecting student classroom behaviors in classroom videos is beneficial for analyzing students' classroom performance and consequently enhancing teaching effectiveness. To address challenges such as object density, occlusion, and multi-scale scenarios in classroom video images, this paper introduces an improved YOLOv8 classroom detection model. Firstly, by combining modules from the Res2Net and YOLOv8 network models, a novel C2f_Res2block module is proposed. This module, along with MHSA and EMA, is integrated into the YOLOv8 model. Experimental results on a classroom detection dataset demonstrate that the improved model in this paper exhibits better detection performance compared to the original YOLOv8, with an average precision (mAP@0.5) increase of 4.2%. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the poor recognition effect and low recognition rate of the existing methods in the process of belt deviation detection, this paper proposes a real-time belt deviation detection method. Firstly, ResNet18 combined with the attention mechanism module is used as a feature extraction network to enhance the features in the belt edge region and suppress the features in other regions. Then, the extracted features are used to predict the approximate locations of the belt edges using a classifier based on the contextual information on the fully connected layer. Next, the improved gradient equation is used as a structural loss in the model training stage to make the model prediction value closer to the target value. Then, the authors of this paper use the least squares method to fit the set of detected belt edge line points to obtain the accurate belt edge straight line. Finally, the deviation threshold is set according to the requirements of the safety production code, and the fitting results are compared with the threshold to achieve the belt deviation detection. Comparisons are made with four other methods: ultrafast structure-aware deep lane detection, end-to-end wireframe parsing, LSD, and the Hough transform. The results show that the proposed method is the fastest at 41 frames/sec; the accuracy is improved by 0.4%, 13.9%, 45.9%, and 78.8% compared to the other four methods; and the F1-score index is improved by 0.3%, 10.2%, 32.6%, and 72%, respectively, which meets the requirements of practical engineering applications. The proposed method can be used for intelligent monitoring and control in coal mines, logistics and transport industries, and other scenarios requiring belt transport. [ABSTRACT FROM AUTHOR]

The "Internet-of-Medical-Vehicles (IOMV)" is one of the special applications of the Internet of Things resulting from combining connected healthcare and connected vehicles. As the IOMV communicates with a variety of networks along its travel path, it incurs various security risks due to sophisticated cyber-attacks. This can endanger the onboard patient's life. So, it is critical to understand subjects related to "cybersecurity" in the IOMV to develop robust cybersecurity measures. In this paper, the goal is to evaluate recent trends and state-of-the-art publications, gaps, and future outlooks related to this research area. With this aim, a variety of publications between 2016 and 2023 from "Web-of-Science" and "Scopus" databases were analysed. Our analysis revealed that the IOMV is a niche and unexplored research area with few defined standards and frameworks, and there is a great need to implement robust cybersecurity measures. This paper will help researchers to gain a comprehensive idea of this niche research topic, as it presents an analysis of top journals and highly cited papers, their challenges and limitations, the system model and architecture of the IOMV, related applicable standards, potential cyber-attacks, factors causing cybersecurity risks, various artificial intelligence techniques for developing potential countermeasures, the assessment and parameterisation of cybersecurity risks, constraints and challenges, and future outlooks for implementing cybersecurity measures in the IOMV. [ABSTRACT FROM AUTHOR]

36616732 16 Lupenko S., Butsiy R., Shakhovska N. Advanced Modeling and Signal Processing Methods in Brain-Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes. 34640663 4 Martinek R., Ladrova M., Sidikova M., Jaros R., Behbehani K., Kahankova R., Kawala-Sterniuk A. Advanced bioelectrical signal processing methods: Past, present, and future approach - Part III: Other biosignals. 34502642 2 Martinek R., Ladrova M., Sidikova M., Jaros R., Behbehani K., Kahankova R., Kawala-Sterniuk A. Advanced bioelectrical signal processing methods: Past, present and future approach - Part I: Cardiac signals. Analysis of biomedical data can provide useful information regarding human condition and as a result - analysis of these signals has become one of the most popular diagnostic methods. [Extracted from the article]

The advent of the Internet of Things (IoT) has contributed to an increase in the production volume of RF-featured equipment. According to statistics from the literature, the IoT industry will soon deploy billions of products. While the concept behind these applications seems exciting, this paper sought to assess the effects the radio emissions produced by IoT products would have on the ambient radio noise levels within the unlicensed frequency bands of 433 MHz, 868 MHz, and 2.4 GHz. The study extended to three environments: industrial, urban, and suburban. This study developed an IoT noise generator (ING) device to emulate RF noise signals in the desired IoT radio transmission band. The paper presents a simplified radio noise surveying system (RNSS) for data collection of ambient radio noise from five South African candidate sites. The statistical and empirical analysis agree that the level of ambient radio noise was directly proportional to the rate of IoT radio activities. The slopes of the regression lines demonstrate that 80% of the analyzed data developed augmenting trends. Approximately 20% of the data show declining trends. [ABSTRACT FROM AUTHOR]

Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the "masked phase autoencoders with a vision transformer network" (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time. [ABSTRACT FROM AUTHOR]

The development and growth of Wireless Sensor Networks (WSNs) is significantly propelled by advances in Radio Frequency (RF) and Visible Light Communication (VLC) technologies. This paper endeavors to present a comprehensive review of the state-of-the-art in cognitive hybrid RF-VLC systems for WSNs, emphasizing the critical task of seamlessly integrating Cognitive Radio Sensor Networks (CRSNs) and VLC technologies. The central challenge addressed is the intricate landscape of this integration, characterized by notable trade-offs between performance and complexity, which escalate with the addition of more devices and increased data rates. This scenario necessitates the development of advanced cognitive radio strategies, potentially facilitated by Machine Learning (ML) and Deep Learning (DL) approaches, albeit introducing new complexities such as the necessity for pre-training with extensive datasets. The review scrutinizes the fundamental aspects of CRSNs and VLC, spotlighting key areas like Energy Efficient Resource Allocation, Industrial Scenarios, and Energy Harvesting, and explores the synergistic amalgamation of these technologies as a promising pathway for enhanced spectrum utilization and network performance. By delving into the integration of cognitive radio technology with visible light, this study furnishes valuable insights into the potential for innovative applications in wireless communication, presenting a balanced overview of the current advancements and prospective avenues in the field of cognitive hybrid RF/VLC systems. [ABSTRACT FROM AUTHOR]

Driverless technology refers to the technology that vehicles use to drive independently with the help of driverless system under the condition of unmanned intervention. The working environment of construction machinery is bad, and the working conditions are complex. The use of driverless technology can greatly reduce the risk of driver operation, reduce labor costs and improve economic benefits.Aiming at the problem of the GPS positioning signal in the working environment of construction machinery being weak and not able to achieve accurate positioning, this paper uses the fusion SLAM algorithm based on improved NDT to realize the real-time positioning of the whole vehicle through reconstruction of the scene. Considering that the motion characteristics of crawler construction machinery are different from those of ordinary passenger cars, this paper improves the existing pure pursuit algorithm. Simulations and real vehicle tests show that the algorithm combined with the fusion SLAM algorithm can realize the motion control of driverless crawler construction machinery well, complete the tracking of the set trajectory perfectly and have high robustness. Considering that there is no mature walking method of driverless crawler construction machinery for reference, the research of this paper will lay a foundation for the development of driverless crawler construction machinery. [ABSTRACT FROM AUTHOR]

The ability to simultaneously heat and image samples using transmitted light is crucial for several biological applications. However, existing techniques such as heated stage microscopes, thermal cyclers equipped with imaging capabilities, or non-contact heating systems are often bulky, expensive, and complex. This work presents the development and characterization of a Miniaturized Optically-clear Thermal Enclosure (MOTE) system—an open-source, inexpensive, and low-powered modular system—capable of convectively heating samples while simultaneously imaging them with transmitted light. We develop and validate a computational fluid dynamics (CFD) model to design and optimize the heating chamber. The model simulates velocity and temperature profiles within the heating chamber for various chamber materials and sizes. The computational model yielded an optimal chamber dimension capable of achieving a stable temperature ranging from ambient to 95 °C with a spatial discrepancy of less than 1.5 °C, utilizing less than 8.5 W of power. The dual-functionality of the MOTE system, enabling synchronous heating and transmitted light imaging, was demonstrated through the successful execution of paper-based LAMP reactions to detect λ DNA samples in real-time down to 10 copies/µL of the target concentration. The MOTE system offers a promising and flexible platform for various applications, from molecular diagnostics to biochemical analyses, cell biology, genomics, and education. [ABSTRACT FROM AUTHOR]

This paper presents a VIDAR (a Vision-IMU based detection and ranging method)-based approach to road-surface pothole detection. Most potholes on the road surface are caused by the further erosion of cracks in the road surface, and tires, wheels and bearings of vehicles are damaged to some extent as they pass through the potholes. To ensure the safety and stability of vehicle driving, we propose a VIDAR-based pothole-detection method. The method combines vision with IMU to filter, mark and frame potholes on flat pavements using MSER to calculate the width, length and depth of potholes. By comparing it with the classical method and using the confusion matrix to judge the correctness, recall and accuracy of the method proposed in this paper, it is verified that the method proposed in this paper can improve the accuracy of monocular vision in detecting potholes in road surfaces. [ABSTRACT FROM AUTHOR]

The idea of the person re-identification (Re-ID) task is to find the person depicted in the query image among other images obtained from different cameras. Algorithms solving this task have important practical applications, such as illegal action prevention and searching for missing persons through a smart city's video surveillance. In most of the papers devoted to the problem under consideration, the authors propose complex algorithms to achieve a better quality of person Re-ID. Some of these methods cannot be used in practice due to technical limitations. In this paper, we propose several approaches that can be used in almost all popular modern re-identification algorithms to improve the quality of the problem being solved and do not practically increase the computational complexity of algorithms. In real-world data, bad images can be fed into the input of the Re-ID algorithm; therefore, the new Filter Module is proposed in this paper, designed to pre-filter input data before feeding the data to the main re-identification algorithm. The Filter Module improves the quality of the baseline by 2.6 % according to the R a n k 1 metric and 3.4 % according to the m A P metric on the Market-1501 dataset. Furthermore, in this paper, a fully automated data collection strategy from surveillance cameras for self-supervised pre-training is proposed in order to increase the generality of neural networks on real-world data. The use of self-supervised pre-training on the data collected using the proposed strategy improves the quality of cross-domain upper-body Re-ID on the DukeMTMC-reID dataset by 1.0 % according to the R a n k 1 and m A P metrics. [ABSTRACT FROM AUTHOR]

Deep learning pervades heavy data-driven disciplines in research and development. The Internet of Things and sensor systems, which enable smart environments and services, are settings where deep learning can provide invaluable utility. However, the data in these systems are very often directly or indirectly related to people, which raises privacy concerns. Federated learning (FL) mitigates some of these concerns and empowers deep learning in sensor-driven environments by enabling multiple entities to collaboratively train a machine learning model without sharing their data. Nevertheless, a number of works in the literature propose attacks that can manipulate the model and disclose information about the training data in FL. As a result, there has been a growing belief that FL is highly vulnerable to severe attacks. Although these attacks do indeed highlight security and privacy risks in FL, some of them may not be as effective in production deployment because they are feasible only given special—sometimes impractical—assumptions. In this paper, we investigate this issue by conducting a quantitative analysis of the attacks against FL and their evaluation settings in 48 papers. This analysis is the first of its kind to reveal several research gaps with regard to the types and architectures of target models. Additionally, the quantitative analysis allows us to highlight unrealistic assumptions in some attacks related to the hyper-parameters of the model and data distribution. Furthermore, we identify fallacies in the evaluation of attacks which raise questions about the generalizability of the conclusions. As a remedy, we propose a set of recommendations to promote adequate evaluations. [ABSTRACT FROM AUTHOR]

The aim of the present work was to develop a method based on image analysis for describing soil detachment caused by the impact of a single water drop. The method consisted of recording tracks made by splashed particles on blotting paper under an optical microscope. The analysis facilitated division of the recorded particle tracks on the paper into drops, "comets" and single particles. Additionally, the following relationships were determined: (i) the distances of splash; (ii) the surface areas of splash tracks into relation to distance; (iii) the surface areas of the solid phase transported over a given distance; and (iv) the ratio of the solid phase to the splash track area in relation to distance. Furthermore, the proposed method allowed estimation of the weight of soil transported by a single water drop splash in relation to the distance of the water drop impact. It was concluded that the method of image analysis of splashed particles facilitated analysing the results at very low water drop energy and generated by single water drops. [ABSTRACT FROM AUTHOR]

Food safety and access to systematic approaches for ensuring detection of food hazards is an important issue in most developing countries. With the arrival of paper-based analytical devices (µPADs) as a promising, rapid, easy-to-use, and low-cost analytical tool, we demonstrated a simple microfluidic-based titration study for the analysis of packaged fruit juices. Similar, to the titration experiments using traditional glassware in chemistry laboratories, in this study the titration experiments were developed using paper microfluidics for the analysis of several analytes such as pH, vitamin C, sugars, and preservatives present in the packaged fruit juices. The allergen found commonly in dairy based mixtures and the non-pathogenic biochemical component responsible for food spoilage in cider based fruit juices were also determined. The results obtained using paper microfluidics were compared with those obtained using a conventional spectrophotometric technique. Finally, a paper microfluidics based multiplexed sensor was developed for the analysis of common nutritional ingredients, an allergen, and a non-pathogenic byproduct present in packaged fruit juices on a single platform. Overall, the results presented in this study reveal that the proposed paper microfluidic assisted colorimetric multiplexed sensor offers a quick and reliable tool for on-spot routine analysis for food safety applications. [ABSTRACT FROM AUTHOR]

In this work, we developed and fabricated a paper-based anisotropic magneto-resistive sensor using a sputtered permalloy (Ni 81 Fe 19 ) thin film. To interpret the characteristics of the sensor, we proposed a computational model to capture the influence of the stochastic fiber network of the paper surface and to explain the physics behind the empirically observed difference in paper-based anisotropic magneto-resistance (AMR). Using the model, we verified two main empirical observations: (1) The stochastic fiber network of the paper substrate induces a shift of 45 ∘ in the AMR response of the paper-based Ni 81 Fe 19 thin film compared to a Ni 81 Fe 19 film on a smooth surface as long as the fibrous topography has not become buried. (2) The ratio of magnitudes of AMR peaks at different anisotropy angles and the inverted AMR peak at the 90 ∘ -anisotropy angle are explained through the superposition of the responses of Ni 81 Fe 19 inheriting the fibrous topography and smoother Ni 81 Fe 19 on buried fibrous topographies. As for the sensitivity and reproducibility of the sensor signal, we obtained a maximum AMR peak of 0.4 % , min-max sensitivity range of [ 0.17 , 0.26 ] % , average asymmetry of peak location of 2.7 kA m within two consecutive magnetic loading cycles, and a deviation of 250–850 A m of peak location across several anisotropy angles at a base resistance of ∼100 Ω. Last, we demonstrated the usability of the sensor in two educational application examples: a textbook clicker and interactive braille flashcards. [ABSTRACT FROM AUTHOR]

Electro-active paper (EAPap) is a cellulose-based smart material that has shown promising results in a variety of smart applications (e.g., vibration sensor, piezo-speaker, bending actuator) with the merits of being flexible, lightweight, fracture tolerant, biodegradable, naturally abundant, cheap, biocompatible, and with the ability to form hybrid nanocomposites. This paper presents a review of the characterization and application of EAPap as a flexible mechanical vibration/strain sensor, bending actuator, and vibration energy harvester. The working mechanism of EAPap is explained along with the various parameters and factors that influence the sensing, actuation, and energy harvesting capabilities of EAPap. Although the piezoelectricity of EAPap is comparable to that of commercially available polyvinylidene fluoride (PVDF), EAPap has the preferable merits in terms of natural abundance and ample capacity of chemical modification. The article would provide guidelines for the characterization and application of EAPap in mechanical sensing, actuation, and vibration energy scavenging, along with the possible limitations and future research prospects. [ABSTRACT FROM AUTHOR]

The detailed design considerations for the printed RFID-based sensor system is presented in this paper. Starting from material selection and metallization method, this paper discusses types of RFID-based sensors (single- & dual-tag sensor topologies), design procedures, and performance evaluation methods for the wireless sensor system. The electrical properties of the paper substrates (cellulose-based and synthetic papers) and the silver nano-particle-based conductive film are thoroughly characterized for RF applications up to 8 GHz. The reported technology could potentially set the foundation for truly “green”, low-cost, scalable wireless topologies for autonomous Internet-of-Things (IoT), bio-monitoring, and “smart skin” applications. [ABSTRACT FROM AUTHOR]

In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc. [ABSTRACT FROM AUTHOR]

This study presents the design and fabrication of a flexible tactile sensor printed on a cellulose paper substrate using a carbon black (CB) – filled polyvinyl alcohol (PVA) polymer matrix as ink material. In the design, electrodes are obtained by screen printing of CB/PVA composite on dielectric cellulose paper. The screen-printing method is preferred for fabrication because of its simplicity and low manufacturing cost. The tactile sensor is formed by overlapping two ink-printed sheets. Electrical properties are investigated under compressive and tensile strains. The results indicate that the tactile sensor configuration and materials can be used for piezoresistive, capacitive, and also impedance sensors. The same tactile sensor structure is also examined using a commercial carbon-based ink for performance comparison. The comparative study indicates that CB/PVA ink screen-printed on paper demonstrates superior sensitivity for capacitive sensing with low hysteresis, as well as low response and recovery times. The piezoresistive-sensing properties of CB/PVA on cellulose paper show a gauge factor (GF) of 10.68, which is also very promising when conventional metal strain gauges are considered. CB/PVA screen-printed on cellulose paper features impedance-sensing properties and is also sensitive to the measurement frequency. Therefore, the response type of the sensor can be altered with the frequency. [ABSTRACT FROM AUTHOR]

With the growing significance of printed sensors on the electronics market, new demands on quality and reproducibility have arisen. While most printing processes on standard substrates (e.g., Polyethylene terephthalate (PET)) are well-defined, the printing on substrates with rather porous, fibrous and rough surfaces (e.g., uncoated paper) contains new challenges. Especially in the case of inkjet-printing and other deposition techniques that require low-viscous nanoparticle inks the solvents and deposition materials might be absorbed, inhibiting the formation of homogeneous conductive layers. As part of this work, the sheet resistance of sintered inkjet-printed conductive silver (Ag-) nanoparticle cross structures on two different, commercially available, uncoated paper substrates using Van-der-Pauw's method is evaluated. The results are compared to the conductivity of well-studied, white heat stabilised and treated PET foil. While the sheet resistance on PET substrate is highly reproducible and the variations are solely process-dependent, the sheet resistance on uncoated paper depends more on the substrate properties themselves. The results indicate that the achievable conductivity as well as the reproducibility decrease with increasing substrate porosity and fibrousness. [ABSTRACT FROM AUTHOR]

For the hundreds of millions of worldwide diabetic patients, glucose test strips are the most important and commonly used tool for monitoring blood glucose levels. Commercial test strips use glucose oxidases as recognition agents, which increases the cost and reduces the durability of test strips. To lower the cost of glucose sensors, we developed a paper-based electrical sensor with molecularly imprinted glucose recognition sites and demonstrated the determination of various glucose concentrations in bovine blood solutions. The sensing electrode is integrated with molecular recognition sites in the conductive polymer. A calibration graph as a function of glucose concentration in aqueous solution was acquired and matched with a correlation coefficient of 0.989. We also demonstrated the determination of the added glucose concentrations ranging from 2.2 to 11.1 mM in bovine blood samples with a linear correlation coefficient of 0.984. This non-enzymatic glucose sensor has the potential to reduce the health care cost of test strips as well as make glucose sensor test strips more accessible to underserved communities. [ABSTRACT FROM AUTHOR]