Organic-Acid-Sensitive Visual Sensor Array Based on Fenton Reagent-Phenol/Aniline for the Rapid Species and Adulteration Assessment of Baijiu.

Baijiu is an ancient, distilled spirit with a complicated brewing process, unique taste, and rich trace components. These trace components play a decisive role in the aroma, taste, and especially the quality of baijiu. In this paper, the redox reaction between the Fenton reagent and four reducing agents, including o-phenylenediamine (OPD), p-phenylenediamine (PPD), 4-aminophenol (PAP), and 2-aminophenol (OAP), was adopted to construct a four-channel visual sensor array for the rapid detection of nine kinds of common organic acids in baijiu and the identification of baijiu and its adulteration. By exploiting the color-changing fingerprint response brought by organic acids, each organic acid could be analyzed accurately when combined with an optimized variable-weighted least-squares support vector machine based on a particle swarm optimization (PSO-VWLS-SVM) model. What is more, this novel sensor also could achieve accurate semi-quantitative analysis of the mixed organic acid samples via partial least squares discriminant analysis (PLSDA). Most importantly, the sensor array could be further used for the identification of baijiu with different species through the PLSDA model and the adulteration assessment with the one-class partial least squares (OCPLS) model simultaneously.

PrivShieldROS: An Extended Robot Operating System Integrating Ethereum and Interplanetary File System for Enhanced Sensor Data Privacy.

With the application of robotics in security monitoring, medical care, image analysis, and other high-privacy fields, vision sensor data in robotic operating systems (ROS) faces the challenge of enhancing secure storage and transmission. Recently, it has been proposed that the distributed advantages of blockchain be taken advantage of to improve the security of data in ROS. Still, it has limitations such as high latency and large resource consumption. To address these issues, this paper introduces PrivShieldROS, an extended robotic operating system developed by InterPlanetary File System (IPFS), blockchain, and HybridABEnc to enhance the confidentiality and security of vision sensor data in ROS. The system takes advantage of the decentralized nature of IPFS to enhance data availability and robustness while combining HybridABEnc for fine-grained access control. In addition, it ensures the security and confidentiality of the data distribution mechanism by using blockchain technology to store data content identifiers (CID) persistently. Finally, the effectiveness of this system is verified by three experiments. Compared with the state-of-the-art blockchain-extended ROS, PrivShieldROS shows improvements in key metrics. This paper has been partly submitted to IROS 2024.

Dual-ligand lanthanide metal-organic framework based ratiometric fluorescent platform for visual monitoring of aminoglycoside residues in food samples.

Herein, a dual-emission Eu metal-organic framework (Eu-MOF) is prepared and used as the ratiometric fluorescence probe for ultrasensitive detection of aminoglycoside antibiotics (AGs). Due to the strong hydrogen bond interactions between AGs and Eu-MOF, the blue emission is enhanced while the red emission has little fluctuation in Eu-MOF with the addition of AGs, thus a good linear relationship with the logarithm of AGs concentrations from 0.001 to 100 µg/mL can be established for quantitative analysis. Good sensitivity with the detection limit of 0.33 ng/mL for apramycin, 0.32 ng/mL for amikacin and 0.30 ng/mL for kanamycin is achieved. The proposed assay demonstrates good selectivity and applicability for determination of AGs in real milk and honey samples. The Eu-MOF materials are further fabricated as fluorescent test papers for facile visual detection. The as-established ratio fluorescence platform offers a portable and economical way for rapid monitoring AGs residues in complex food samples.

Visual Sensor with Host-Guest Specific Recognition and Light-Electrical Co-Controlled Switch.

Perception of UV radiation has important applications in medical health, industrial production, electronic communication, etc. In numerous application scenarios, there is an increasing demand for the intuitive and low-cost detection of UV radiation through colorimetric visual behavior, as well as the efficient and multi-functional utilization of UV radiation. However, photodetectors based on photoconductive modes or photosensitive colorimetric materials are not conducive to portable or multi-scene applications owing to their complex and expensive photosensitive components, potential photobleaching, and single-stimulus response behavior. Here, a multifunctional visual sensor based on the "host-guest photo-controlled permutation" strategy and the "lock and key" model is developed. The host-guest specific molecular recognition and electrochromic sensing platform is integrated at the micro-molecular scale, enabling multi-functional and multi-scene applications in the convenient and fast perception of UV radiation, military camouflage, and information erasure at the macro level of human-computer interaction through light-electrical co-controlled visual switching characteristics. This light-electrical co-controlled visual sensor based on an optoelectronic multi-mode sensing system is expected to provide new ideas and paradigms for healthcare, microelectronics manufacturing, and wearable electronic devices owing to its advantages of signal visualization, low energy consumption, low cost, and versatility.

Nano effect fluorescence visual sensor based on Au-AgNCs: A novel strategy to identify the origin and growth year of Lilium bulbs.

In this study, we developed a cost-effective fluorescence visual sensor strategy based on gold and silver nanocluster (Au-AgNCs) for the rapid identification of the origins and growth years of Lilium bulbs (LB). Au-AgNCs combined with catechins in LB produce aggregation-induced emission (AIE). The catechin content in LB of different origins and growth years varied, resulting in different fluorescence color responses of the sensor system. Furthermore, the RGB values of the fluorescent color were extracted, and the discriminant effect of visual visualisation was verified using the data-driven soft independent modelling of class analogy (DD-SIMCA) and partial least squares discriminant analysis (PLSDA) models. The results showed that the accuracy of DD-SIMCA for identifying LB origins and PLSDA for growth year identification was 100%. These results indicated that the established strategy could accurately identify the quality of LB, which has great potential for application in the rapid and visual identification of other foods.

The highly selective rhodol-based putrescine probe and visual sensors for on-site detection of putrescine in food spoilage.

Putrescine (Butane-1,4-diamine) has been regarded as a vital marker of spoiling protein-rich foods, especially meat and seafood. The detection of putrescine in food is considered a convenient and powerful method for evaluating the degree of spoilage of protein-rich foods. Herein, a novel rhodol-based fluorescent probe RSMA (formyl-rhodol Schiff base with methoxyaniline) was developed to detect putrescine. RSMA exhibited excellent linearity (R2 = 0.9912) in the concentration range of 0-45 µM of putrescine with a detection limit as low as 0.45 µM. Although RSMA had moderate responses to some aliphatic diamines, the selectivity of RSMA for putrescine was one of the best reported in the literature so far. Moreover, RSMA was successfully fabricated to solid-state sensors for on-site detection of putrescine in shrimp, that demonstrated its application in monitoring food spoilage.

Chiral Recognition of D/L-Ribose by Visual and SERS Assessments.

Ribose is the central molecular unit in ribose nucleic acid (RNA). Ribose is a key molecule in the study of many persistent scientific mysteries, such as the origin of life and the chiral homogeneity of biological molecules. Therefore, the chiral recognition of ribose is of great significance. The traditional method of chiral recognition of ribose is HPLC, which is time-consuming, expensive, and can only be operated in the laboratory. There is no report on optical analytical techniques that can quickly detect the chirality of ribose. In this study, a simple and convenient approach for the chiral recognition of ribose has been developed. ß-cyclodextrin(ß-CD)-coated Ag NPs aggregate after adding D-ribose, so that D-/L-ribose can be identified using visual colorimetry and/or surface-enhanced Raman spectroscopy (SERS). The color change visible to the naked eye can readily distinguish the chirality of ribose, while the SERS method can provide the more sensitive analysis of enantiomeric ribose. The advantages of this method are that it is fast, convenient, low cost, and can be operated outside the laboratory. DFT calculations show that D-ribose and cyclodextrin have the same chirality, forming multiple strong hydrogen bonds between them; thus, D/L-ribose will induce different optical effects.

Content-Seam-Preserving Multi-Alignment Network for Visual-Sensor-Based Image Stitching.

As an important representation of scenes in virtual reality and augmented reality, image stitching aims to generate a panoramic image with a natural field-of-view by stitching multiple images together, which are captured by different visual sensors. Existing deep-learning-based methods for image stitching only conduct a single deep homography to perform image alignment, which may produce inevitable alignment distortions. To address this issue, we propose a content-seam-preserving multi-alignment network (CSPM-Net) for visual-sensor-based image stitching, which could preserve the image content consistency and avoid seam distortions simultaneously. Firstly, a content-preserving deep homography estimation was designed to pre-align the input image pairs and reduce the content inconsistency. Secondly, an edge-assisted mesh warping was conducted to further align the image pairs, where the edge information is introduced to eliminate seam artifacts. Finally, in order to predict the final stitched image accurately, a content consistency loss was designed to preserve the geometric structure of overlapping regions between image pairs, and a seam smoothness loss is proposed to eliminate the edge distortions of image boundaries. Experimental results demonstrated that the proposed image-stitching method can provide favorable stitching results for visual-sensor-based images and outperform other state-of-the-art methods.

Electrospun nanofiber-based indicatorpaper sensing platform for fluorescence and visualization detection of norfloxacin.

Norfloxacin (NOR) residues in water pose a serious threat to human health via the food chain, necessitating the development of a rapid on-site antibiotic detection technique. In this work, we utilize electrostatic spinning technology that combines polyacrylonitrile (PAN) fibers and adenosine triphosphate (ATP)-rare earth metal Tb3+ complexes (ATP/Tb) to construct a new ternary film-based sensor for sensitive, quick, and convenient field testing of NOR in water. The operating mechanism is that the ternary system produces gradually enhanced bright green fluorescence at increasing concentrations of NOR. The unique fluorescence property of the ternary systems is attributed to the use of ATP, rather than the commonly used adenosine monophosphate (AMP), to coordinate with Tb3+, which avoided the possible fluorescence quenching from competitive water binding. Benefiting from the PAN nanofiber's superior stability, acid, and alkali resistance, and flexibility as support, the ternary system exhibited a good linear response to NOR in a wide dynamic range of 0.04-30 µM at the detection limit of 16 nM. Additionally, the combination of a smartphone color recognition app allows for quick on-scene NOR detection. This film sensing strategy is instructive for the development of smart and portable sensing platforms for real-time detection of analytes such as antibiotics, pesticide residues, and hazardous materials in water bodies.

YOLO-Weld: A Modified YOLOv5-Based Weld Feature Detection Network for Extreme Weld Noise.

Weld feature point detection is a key technology for welding trajectory planning and tracking. Existing two-stage detection methods and conventional convolutional neural network (CNN)-based approaches encounter performance bottlenecks under extreme welding noise conditions. To better obtain accurate weld feature point locations in high-noise environments, we propose a feature point detection network, YOLO-Weld, based on an improved You Only Look Once version 5 (YOLOv5). By introducing the reparameterized convolutional neural network (RepVGG) module, the network structure is optimized, enhancing detection speed. The utilization of a normalization-based attention module (NAM) in the network enhances the network's perception of feature points. A lightweight decoupled head, RD-Head, is designed to improve classification and regression accuracy. Furthermore, a welding noise generation method is proposed, increasing the model's robustness in extreme noise environments. Finally, the model is tested on a custom dataset of five weld types, demonstrating better performance than two-stage detection methods and conventional CNN approaches. The proposed model can accurately detect feature points in high-noise environments while meeting real-time welding requirements. In terms of the model's performance, the average error of detecting feature points in images is 2.100 pixels, while the average error in the world coordinate system is 0.114 mm, sufficiently meeting the accuracy needs of various practical welding tasks.

Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices.

The global expansion of the Visual Internet of Things (VIoT)'s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.

Colorimetric and visual determination of uric acid based on decolorization of manganese dioxide nanosheet dispersions.

Serum levels of uric acid (UA) play an important role in the prevention of diseases. Developing a rapid and accurate way to detect UA is still a meaningful task. Hence, positively charged manganese dioxide nanosheets (MnO2NSs) with an average latter size of 100 nm and an ultra-thin thickness of below 1 nm have been prepared. They can be well dispersed in water and form stable yellow-brown solutions. The MnO2NSs can be decomposed by UA via redox reaction, leading to a decline of a characteristic absorption peak (374 nm) and a color fading of MnO2NSs solution. On this basis, an enzyme-free colorimetric sensing system for the detection of UA has been developed. The sensing system shows many advantages, including a wide linear range of 0.10-50.0 µmol/L, a limit of quantitation (LOQ) of 0.10 µmol/L, a low limit of detection (LOD) of 0.047 µmol/L (3σ/m), and rapid response without need of strict time control. Moreover, a simple and convenient visual sensor for UA detection has also been developed by adding an appropriate amount of phthalocyanine to provide a blue background color, which helps to increase visual discrimination. Finally, the strategy has been successfully applied to detect UA in human serum and urine samples.

A Hardware-Friendlyand High-Efficiency H.265/HEVC Encoder for Visual Sensor Networks.

Visual sensor networks (VSNs) have numerous applications in fields such as wildlife observation, object recognition, and smart homes. However, visual sensors generate vastly more data than scalar sensors. Storing and transmitting these data is challenging. High-efficiency video coding (HEVC/H.265) is a widely used video compression standard. Compare to H.264/AVC, HEVC reduces approximately 50% of the bit rate at the same video quality, which can compress the visual data with a high compression ratio but results in high computational complexity. In this study, we propose a hardware-friendly and high-efficiency H.265/HEVC accelerating algorithm to overcome this complexity for visual sensor networks. The proposed method leverages texture direction and complexity to skip redundant processing in CU partition and accelerate intra prediction for intra-frame encoding. Experimental results revealed that the proposed method could reduce encoding time by 45.33% and increase the Bjontegaard delta bit rate (BDBR) by only 1.07% as compared to HM16.22 under all-intra configuration. Moreover, the proposed method reduced the encoding time for six visual sensor video sequences by 53.72%. These results confirm that the proposed method achieves high efficiency and a favorable balance between the BDBR and encoding time reduction.

Tetrazolium-Based Visually Indicating Bacteria Sensor for Colorimetric Detection of Point of Contamination.

Protective equipment for detecting bacterial contamination has been in high demand with increasing interest in public health and hygiene. Herein, a fiber-based visually indicating bacteria sensor (VIBS) embedded with iodonitrotetrazolium chloride is developed for the general purpose of detecting live bacteria, and its chromogenic effectiveness is investigated for Gram-negative Escherichia coli and Gram-positive Micrococcus luteus. The developed color intensity is measured by the light absorption coefficient to the scattering coefficient (K/S) based on the Kubelka-Munk equation, and the colorimetric sensitivities of different membranes are examined by calculating the limit of detection (LOD) and the limit of quantification (LOQ). The results demonstrate that the interactions between VIBS and bacteria depend on the wetting properties of membranes. A hydrophobic membrane shows excessive interactions at high concentrations of Gram-negative E. coli bacteria, whose cell membrane is lipophilic. The membrane blended with hydrophobic and hydrophilic polymers displays linear colorimetric responses for both Gram-negative and Gram-positive bacteria strains, demonstrating a reliable sensing capability in the range of the tested bacteria concentration. This study is significant in that explorative experimentations are performed to conceive a proof of concept of a fiber-based bacteria sensor, which is readily applicable in various fields where bacteria pose a threat.

Application of Radar Solutions for the Purpose of Bird Tracking Systems Based on Video Observation.

Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely implemented in small and medium-size airports, as well as on wind turbines is based on a stereo-vision. However, to provide long-term observations allowing the determination of the hot spots of birds' activity and forecast future events, a robust tracking algorithm is required. The aim of this paper is to review tracking algorithms widely used in Radar Science and assess the possibilities of application of these algorithms for the purpose of tracking birds with a stereo-vision system. We performed a survey-of-related works and simulations determined five state-of-the art algorithms: Kalman Filter, Nearest-Neighbour, Joint-Probabilistic Data Association, and Interacting Multiple Model with the potential for implementation in a stereo-vision system. These algorithms have been implemented and simulated in the proposed case study.

Neural Networks for Automatic Posture Recognition in Ambient-Assisted Living.

Human Action Recognition (HAR) is a rapidly evolving field impacting numerous domains, among which is Ambient Assisted Living (AAL). In such a context, the aim of HAR is meeting the needs of frail individuals, whether elderly and/or disabled and promoting autonomous, safe and secure living. To this goal, we propose a monitoring system detecting dangerous situations by classifying human postures through Artificial Intelligence (AI) solutions. The developed algorithm works on a set of features computed from the skeleton data provided by four Kinect One systems simultaneously recording the scene from different angles and identifying the posture of the subject in an ecological context within each recorded frame. Here, we compare the recognition abilities of Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) Sequence networks. Starting from the set of previously selected features we performed a further feature selection based on an SVM algorithm for the optimization of the MLP network and used a genetic algorithm for selecting the features for the LSTM sequence model. We then optimized the architecture and hyperparameters of both models before comparing their performances. The best MLP model (3 hidden layers and a Softmax output layer) achieved 78.4%, while the best LSTM (2 bidirectional LSTM layers, 2 dropout and a fully connected layer) reached 85.7%. The analysis of the performances on individual classes highlights the better suitability of the LSTM approach.

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things.

Internet of Things (IoT) realizes the real-time video monitoring of plant propagation or growth in the wild. However, the monitoring time is seriously limited by the battery capacity of the visual sensor, which poses a challenge to the long-working plant monitoring. Video coding is the most consuming component in a visual sensor, it is important to design an energy-efficient video codec in order to extend the time of monitoring plants. This article presents an energy-efficient Compressive Video Sensing (CVS) system to make the visual sensor green. We fuse a context-based allocation into CVS to improve the reconstruction quality with fewer computations. Especially, considering the practicality of CVS, we extract the contexts of video frames from compressive measurements but not from original pixels. Adapting to these contexts, more measurements are allocated to capture the complex structures but fewer to the simple structures. This adaptive allocation enables the low-complexity recovery algorithm to produce high-quality reconstructed video sequences. Experimental results show that by deploying the proposed context-based CVS system on the visual sensor, the rate-distortion performance is significantly improved when comparing it with some state-of-the-art methods, and the computational complexity is also reduced, resulting in a low energy consumption.

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers.

Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.

A Runway Safety System Based on Vertically Oriented Stereovision.

In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding $200 million annually, rising to $1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup's flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system's high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty.

Fluorescence distinguishing of SO2 derivatives and Cys/GSH from multi-channel signal patterns and visual sensing based on smartphone in living cells and environment.

Sulfur dioxide (SO2), cysteine (Cys) and glutathione (GSH), which perform crucial actions in regulating the balance of human, are closely related reactive sulfur species (RSS). Moreover, SO2 is one of the most concerned air pollutants, which is easily soluble in water and forms its derivatives. Therefore, it is highly desirable to differentiate SO2 derivatives and Cys/GSH in living cells and environment. Herein, a new near-infrared (NIR) mitochondria-targeted fluorescent probe, NIR-CG, which could distinguish SO2 derivatives and Cys/GSH by using multiple sets of signal patterns under single excitation was reported. NIR-CG exhibited different fluorescence signal modes to SO32- and Cys/GSH with low limit of detection (17.1 nM for SO32-, 17.3 nM for Cys and 25.9 nM for GSH). The recognition mechanisms of NIR-CG to SO32- and Cys/GSH were verified by HRMS, 1H NMR and DFT calculation. NIR-CG had good ability of mitochondrial targeted and fluorescence imaging in cells. What's more, NIR-CG showed great recovery rates (101-104%) in the determination of SO32- in actual water samples. It was worth noting that NIR-CG-based paper strip successfully realized the visual quantitative detection of SO32- and Cys/GSH by use of smartphone, which offered a novel method to develop powerful sensing platform.