Monitoring Scenarios Research Articles

Self-healing and transparent ionic conductive PVA/pullulan/borax hydrogels with multi-sensing capabilities for wearable sensors

Conductive hydrogels as wearable sensors have been used for numerous applications in human motion detection, personal healthcare monitoring and other diverse scenarios. However, it remains a challenge to integrate self-healing ability, multiple sensing capabilities, and transparency in one single unit. In this work, multifunctional polyvinyl alcohol (PVA)/Pullulan/Borax conductive hydrogels were fabricated by introducing borate ester bonds and hydrogen bonds. The described hydrogels showed fast self-healing properties, which could autonomously completely recover within 15 s. The hydrogels possessed high optical transparency (92.9%) in the visible light range and had multi-sensing capabilities, such as strain, temperature and humidity sensing. The assembled hydrogel sensor displayed a high strain sensitivity of 2.74 within the strain range of 300%, and it could be used to monitor human motions such as finger and wrist bending. In addition, the hydrogel sensor could sense temperature variations with a temperature coefficient of resistance of −0.914 °C−1 over 28–46 °C. Besides, the hydrogel sensor demonstrated the humidity sensing ability and can recognize human inhale and exhale. The overall sensing performance of the PVA/Pullulan/Borax hydrogel was satisfactory and repeatable. This conductive hydrogel shows great potential in wearable electronics and personal healthcare and inspires a new generation of multifunctional hydrogel sensors.

Локализация подводного источника шума движущимся приемником на основе голографической обработки

The scenario of underwater situation mobile robotic monitoring by a single vector-scalar receiver placed on board a moving autonomous uninhabited underwater vehicle is presented. Underwater noise source detection and localization is based on holographic processing of sonar signals interferograms. An algorithm for restoring a moving underwater source parameters is described. The numerical simulation results are presented. The errors of restoring the source parameters are analyzed.

Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. It critically evaluates the performance of various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, and Hyperion, in assessing key water quality parameters including chlorophyll-a (Chl-a), turbidity, and colored dissolved organic matter (CDOM). This review highlights the specific advantages of each satellite platform, considering factors like spatial and temporal resolution, spectral coverage, and the suitability of these platforms for different lake sizes and characteristics. In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These models are analyzed for their ability to handle the complexities inherent in remote sensing data, including high dimensionality, non-linear relationships, and the integration of multispectral and hyperspectral data. This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. Moreover, this paper identifies and discusses the key challenges associated with data quality, model interpretability, and integrating remote sensing imagery with machine learning models. It emphasizes the need for advancements in data fusion techniques, improved model generalizability, and the developing robust frameworks for integrating multi-source data. This review concludes by offering targeted recommendations for future research, highlighting the potential of interdisciplinary collaborations to enhance the application of these technologies in sustainable lake water quality management.

Future Low-Cost Urban Air Quality Monitoring Networks: Insights from the EU’s AirHeritage Project

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue.

Validation of sampling points for airborne radioactivity in particulate-generating operations

This study introduces an approach to validate sampling points for air monitoring in environments where operations generate significant airborne particulate radioactivity. A case study of repair works in a nuclear spent fuel reprocessing facility is used to synthesis and demonstrate the methodology. We use the probability distribution of air activity measurements and the correlation between two sampling points near high particulate generating operations and the ventilation ducts. The methodology developed in this paper can be applied in task-related air monitoring scenarios for validation of sampling points. This will augment internal exposure control measures during works involving physical entries in areas with high potential of escalation of air activity (e.g., during major repairs, during decommissioning of nuclear facilities etc.) and to evaluate the sufficiency of respiratory protection.

An adaptive parameter-free seismic data denoising approach by combining general cross-validation thresholding and pixel connectivity in synchrosqueezed domain

High signal-to-noise ratio (SNR) seismic waveform data are conductive to various studies in seismology. Seismic denoising aims to enhance SNR by eliminating additive noise through signal processing while preserving important features of the seismic signal. Conventional parametric seismic denoising methods often require selecting appropriate parameters to achieve optimal results, which may be limiting when dealing with various types and scales of seismic data. Here, we develop an adaptive parameter-free denoising method by combining general cross-validation (GCV) thresholding and pixel connectivity in synchrosqueezed (SS) domain. In this denoising framework, the synchrosqueezed continuous wavelet transform (SS-CWT) is first applied to obtain a high-resolution time–frequency representation. Then, the GCV approach, which allows for choosing the (nearly) optimal threshold without relying on any prior knowledge about the noise level, is employed to attenuate most of the low-energy noise. After that, the relatively isolated high-energy residual noise remaining in the SS-CWT spectrum is removed using pixel connectivity thresholding. Finally, the inverse SS-CWT is applied to the thresholded spectrum to obtain the denoised seismic record. As the thresholds for GCV and pixel connectivity are derived from the spectrum characteristics of the data being analyzed, the proposed denoising approach is highly adaptive and parameter-free. We demonstrate the effectiveness and versatility of the proposed denoising framework using synthetic data and real seismic data from diverse monitoring scenarios, including land, ocean, and emerging distributed acoustic sensing (DAS). The results indicate that the method is a stable and efficient tool for seismic data denoising.Graphical

Road Defect Identification and Location Method Based on an Improved ML-YOLO Algorithm.

The conventional method for detecting road defects relies heavily on manual inspections, which are often inefficient and struggle with precise defect localization. This paper introduces a novel approach for identifying and locating road defects based on an enhanced ML-YOLO algorithm. By refining the YOLOv8 object detection framework, we optimize both the traditional convolutional layers and the spatial pyramid pooling network. Additionally, we incorporate the Convolutional Block Attention to effectively capture channel and spatial features, along with the Selective Kernel Networks that dynamically adapt to feature extraction across varying scales. An optimized target localization algorithm is proposed to achieve high-precision identification and accurate positioning of road defects. Experimental results indicate that the detection accuracy of the improved ML-YOLO algorithm reaches 0.841, with a recall rate of 0.745 and an average precision of 0.817. Compared to the baseline YOLOv8 model, there is an increase in accuracy by 0.13, a rise in recall rate by 0.117, and an enhancement in average precision by 0.116. After the high detection accuracy of road defects was confirmed, generalization experiments were carried out on the improved ML-YOLO model in the public data set. The experimental results showed that compared with the original YOLOv8n, the average precision and recall rate of all types of ML-YOLO increased by 0.075, 0.121, and 0.035 respectively, indicating robust generalization capabilities. When applied to real-time road monitoring scenarios, this algorithm facilitates precise detection and localization of defects while significantly mitigating traffic accident risks and extending roadway service life. A high detection accuracy of road defects was achieved.

Deep Learning-Based Camouflaged Object Detection and Tracking for Enhanced Video Surveillance

To improve security and tactical awareness, video surveillance systems need to be able to find and track objects that are hidden. The approach suggests a deep learning-based method for finding and following hidden objects. There are two major steps in our method: finding and following. During the detecting step, we use a R-CNN based model to find items that are hidden in each frame. A lot of labelled pictures with different kinds of camouflage patterns and objects are used to train this model. The updated Faster R-CNN has more levels and changes that make it better at finding things that are hidden. To keep track of the objects across frames, we use an LSTM-based tracker. The LSTM tracker uses the object's traits and motion information to keep track of its state and guess where it will be in the next frame. To make our method even more effective, we use pre-trained models like MobileNet and VGG-19 for feature extraction. This makes the computations simpler while keeping the accuracy of the recognition and tracking high. The proposed model tested on a number of difficult video scenes with items that were well hidden. The results show that our method works better than other methods, as it increases the accuracy of both recognition and tracking. Also, our method works well even when the lighting changes, the size of the objects changes, or the background is complicated. The suggested method based on deep learning looks like a good way to find and follow hidden objects in video monitoring scenarios. Using CNNs, LSTM networks, and pre-trained models like MobileNet and VGG-19, we show big improvements in terms to compare the various performance parameters with accuracy and stability, which helps make security and monitoring systems better.

DLCH-YOLO: An Object Detection Algorithm for Monitoring the Operation Status of Circuit Breakers in Power Scenarios

In the scenario of power system monitoring, detecting the operating status of circuit breakers is often inaccurate due to variable object scales and background interference. This paper introduces DLCH-YOLO, an object detection algorithm aimed at identifying the operating status of circuit breakers. Firstly, we propose a novel C2f_DLKA module based on Deformable Large Kernel Attention. This module adapts to objects of varying scales within a large receptive field, thereby more effectively extracting multi-scale features. Secondly, we propose a Semantic Screening Feature Pyramid Network designed to fuse multi-scale features. By filtering low-level semantic information, it effectively suppresses background interference to enhance localization accuracy. Finally, the feature extraction network incorporates Generalized-Sparse Convolution, which combines depth-wise separable convolution and channel mixing operations, reducing computational load. The DLCH-YOLO algorithm achieved a 91.8% mAP on our self-built power equipment dataset, representing a 4.7% improvement over the baseline network Yolov8. With its superior detection accuracy and real-time performance, DLCH-YOLO outperforms mainstream detection algorithms. This algorithm provides an efficient and viable solution for circuit breaker status detection.

An Invisible Dermal Nanotattoo-Based Smart Wearable Sensor for eDiagnostics of Jaundice.

Despite substantial progress in the diagnosis of jaundice/hyperbilirubinemia as the most common disease and cause of hospitalization of newborns, on the eve of Industry/Healthcare 5.0, the development of accurate and reliable wearable diagnostic sensors for noninvasive smart monitoring of bilirubin (BIL) is still in high demand. Aiming to fabricate a smart wearable sensor for early diagnosis of neonatal jaundice and its therapeutic monitoring, we here report a fluorescent dermal nanotattoo that further coupled with an IoT-integrated wearable optoelectronic reader for minimally invasive, continuous, and real-time monitoring of BIL in interstitial fluid. Selective recovery of quenched fluorescence of the dermal tattoo sensor, composed of biocompatible dissolving/hydrogel microneedles loaded with fluorescent carbon quantum dots, upon blue light exposure used for jaundice phototherapy was utilized for highly selective BIL sensing. The fascinating features of our developed smart wearable tattoo sensor and its successful results with high correlation with blood BIL results make it a highly promising sensor for easy, minimally invasive, reliable, and smart eDiagnostics and continuous therapeutic eMonitoring of jaundice and other BIL-induced diseases at the point of care. We envision that the developed nanotattoo sensing bioplatform will inspire the development of future smart tattoo sensors in various diagnostic and monitoring scenarios.

Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy.

Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST). A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario. The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16). Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Advancements in monitoring: a comparison of traditional and application-based tools for measuring outdoor recreation.

Outdoor recreation has experienced a boom in recent years and continues to grow. While outdoor recreation provides wide-ranging benefits to human well-being, there are growing concerns about the sustainability of recreation with the increased pressures placed on ecological systems and visitor experiences. These concerns emphasize the need for managers to access accurate and timely recreation data at scales that match the growing extent of the recreation footprint. Here, we compare spatial and temporal patterns of winter and summer recreation using traditional (trail cameras, infrared counters, aerial surveys, participatory mapping) and application-based tools (Strava Metro, Strava Global Heatmap, Wikiloc) across the Columbia and Canadian Rocky Mountains of western Canada. We demonstrate how recreation use can be estimated using traditional and application-based tools, although their accuracy and utility varies across space, season and activity type. We found that trail cameras and infrared counters captured similar broad-scale patterns in count estimates of pedestrians and all recreation activities. Aerial surveys captured areas with low recreation intensity and participatory mapping captured coarser information on the intensity and extent of recreation across large spatial and temporal scales. Application-based data provided detailed spatiotemporal information on recreation use, but datasets were biased towards specific activities. Strava Metro data was more suited for capturing broad-scale spatial patterns in biking than pedestrian recreation. Application-based data should be supplemented with data from traditional tools to identify biases in data and fill in data gaps. We provide a comparison of each tool for measuring recreation use, highlight each tools' strengths and limitations and applications to address real-world monitoring and management scenarios. Our research contributes towards a better understanding of which tool, or combinations of tools, to use that can expand the rigor and scope of recreation research. These findings support decision-making to mitigate pressures on wildlife and their habitats while allowing for high-quality recreation experiences.

Achieving Real-Time Prediction of Paroxysmal Atrial Fibrillation Onset by Convolutional Neural Network and Sliding Window on R-R Interval Sequences.

Early diagnosis of paroxysmal atrial fibrillation (PAF) could prompt patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but are challenged by real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning-based PAFNet model that integrates a sliding window technique on raw R-R intervals of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onset. This integration enables the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, and specifically its effectiveness in making a new prediction with each new heartbeat. Catering to the potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of the automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments were collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with 100 R-R intervals was achieved, with a sensitivity of 97.12%, a specificity of 97.77%, and an accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding window step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 min in advance. The present results suggest that the sliding window technique on raw R-R interval sequences, along with deep learning-based algorithms, may offer the possibility of providing an accurate, real-time, end-to-end clinical tool for mass monitoring of PAF.

Machine-learning models for shoulder rehabilitation exercises classification using a wearable system.

The objective of this study is to train and test machine-learning (ML) models to automatically classify shoulder rehabilitation exercises. The cohort included both healthy and patients with rotator-cuff (RC) tears. All participants performed six shoulder rehabilitation exercises, following guidelines developed by the American Society of Shoulder and Elbow Therapists. Each exercise was repeated six times, while wearing a wearable system equipped with three magneto-inertial sensors. Six supervised machine-learning models (k-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest (RF), Logistic Regression and Adaptive Boosting) were trained for the classification. The algorithms' ability to accurately classify exercise activities was evaluated using the nested cross-validation method, with different combinations of outer and inner folds. A total of 19 healthy subjects and 17 patients with complete RC tears were enroled in the study. The highest classification performances were achieved by the RF classifier, with an accuracy of 89.91% and an F1-score of 89.89%. The results of this study highlight the feasibility and effectiveness of using wearable sensors and ML algorithms to accurately classify shoulder rehabilitation exercises. These findings suggest promising prospects for implementing the proposed wearable system in remote home-based monitoring scenarios. The ease of setup and modularity of the system reduce user burden enabling patient-driven sensor positioning. Level III.

Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner.

This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios.

Development and evaluation of a colorimetric LAMP based-assay targeting the Bacteroides HF183 marker for tracking sewage pollution in environmental waters

Surface waters are vulnerable to contamination by human and animal feces, posing risks to human health due to potential exposure to enteric pathogens. This research developed a colorimetric loop-mediated isothermal amplification (cLAMP) assay to detect sewage associated Bacteroides dorei HF183/BacR287 (HF183) marker in wastewater and environmental water samples. The host sensitivity and host specificity of the assay were evaluated, and their performance was compared to the Bacteroides HF183 qPCR assay using control materials (gBlocks), environmental water samples seeded with untreated sewage, and ambient environmental water samples. In serial dilutions of control materials, qPCR produced quantifiable data across all dilutions, while cLAMP detected the marker down to 0.001 pg/µL of control materials, which was two orders of magnitude less sensitive than qPCR. All untreated sewage samples (n = 12) tested positive for HF183 by both the qPCR and cLAMP assays, demonstrating a host sensitivity value of 1.00 (maximum value of 1.00). The host specificity by analysing 70 non-human fecal nucleic acid samples revealed cLAMP's specificity value of 0.81 compared to qPCR's 0.64. When testing sewage-seeded environmental water samples, both methods detected HF183 for the lowest amount of sewage, indicating similar detection sensitivity. The application of cLAMP for tracking sewage pollution in environmental waters showed promising results, with moderate agreement between cLAMP and qPCR (κ = 0.510). However, cLAMP occasionally missed detections compared to qPCR, particularly in low-concentration samples. Overall, the cLAMP HF183 assay demonstrated promising potential as a rapid and sensitive method for detecting sewage pollution, offering a viable alternative to qPCR in certain environmental monitoring scenarios.

Relation between fault characteristic frequencies and local interpretability shapley additive explanations for continuous machine health monitoring

Recently, the Shapley additive explanations models have been extensively studied to enhance explainability of artificial intelligence algorithms, while most of them simply use Shapley additive explanations to rank or measure the importance of different features. In this study, a novel methodology that studies the relation between fault characteristic frequencies and Shapley values generated by local interpretability Shapley additive explanations for machine health monitoring is proposed. Firstly, a simulation model is introduced to generate vibration signals at different health conditions and their spectral amplitudes transformed from Fourier transform are used to investigate the relationship between fault characteristic frequencies and local interpretability Shapley values. It is interestingly found that Shapley values can be used to locate fault characteristic frequencies. Moreover, most of them have negative values in a normal stage and have positive values in an abnormal stage. Based on this finding and Shapley additive explanations, a health indicator construction methodology is proposed to continuously monitor incipient machine faults. Subsequently, an automatic signal filtering method is proposed to remove and eliminate burrs and noise in Shapley values so that fault characteristic frequencies can be clearly revealed by Shapley values for physical fault diagnosis. Two run-to-failure cases are conducted to demonstrate the effectiveness of the proposed methodology and then the superiority of this study is demonstrated by comparing with existing methods for health indicator construction and fault diagnosis, including sparsity parameters, Hjorth parameters, and fast Kurtogram. Comparison results show that the proposed health indicator is more sensitive to the time of incipient fault initiation and interpretable fault diagnosis based on Shapley values has a robust performance. This study first sheds a light on the relationship between fault characteristic frequencies and Shapley values under the scenario of continuous machine health monitoring and seamlessly guides applicants to realize Shapley additive explanations based incipient fault detection and diagnosis.

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge. This study proposes a method for inter-patient heartbeat classification by fusing dual-channel squeeze-and-excitation residual neural networks (SE-ResNet) and expert features. In the preprocessing stage, ECG heartbeats extracted from both leads of ECG signals are filtered and normalized. Additionally, nine features representing waveform morphology and heartbeat contextual information are selected to be fused with the deep neural networks. Using different filter and kernel sizes for each block, the SE-residual block-based model can effectively learn long-term features between heartbeats. The divided ECG heartbeats and extracted features are then input to the improved SE-ResNet for training and testing according to the inter-patient scheme. The focal loss is utilized to handle the heartbeat of the imbalance category. The proposed arrhythmia classification method is evaluated on three open-source databases, and it achieved an overall F1-score of 83.39 % in the MIT-BIH database. This system can be applied in the scenario of daily monitoring of ECG and plays a significant role in diagnosing arrhythmias.

Securing Anomaly Detection for Process-Based Time Series

Widespread innovation from artificial intelligence and machine learning (AI/ML) tools presents a lucrative opportunity for the nuclear industry to improve state-of-the-art analyses (e.g. condition monitoring, remote operation, etc.) due to increased data visibility. In recent years, the risk posed by collaborative data exchange has received increased attention due, in part, to a potential adversary’s ability to reverse-engineer intercepted data using domain knowledge and AI/ML tools. While the efficacy of typical encryption has been proven during passive communication and data storage, collaborative exchange typically requires decryption for extended analyses by a third-party, which poses an intrinsic risk due to these trustworthiness concerns. The directed infusion of data (DIOD) 1 1 A.Al Rashdan and H.Abdel-Khalik, Deceptive Infusion of Data, Non-Provisional Patent, Application No. 63/227,389, September 2022. paradigm presented in this paper discusses a novel data masking technique that relies on preserving the usable information of proprietary data while concealing its identity via reduced-order modeling. In contrast to existing state-of-the-art data masking methods, DIOD does not impose limiting assumptions, computational overheads, or induced uncertainties, thereby allowing for secure and flexible data-level security that does not alter the inferential content of the data. This paper focuses on the application of DIOD to a process-based simulation wherein a leaking reservoir with a controlled inlet pump is simulated under various experimental conditions with the goal of producing masked data that preserve the information given by anomalies. These experiments included the injection of statistically significant anomalies, subtle anomalies that occurred over an extended period, and the addition of several independent anomalous states. Each experiment showed that a classifier will identify the same anomalies whether it analyzes the original or masked data. An additional experiment also tested the case of corrupted labeling information wherein labels were arbitrarily randomized, and the loss in labeling accuracy was about the same for both datasets. Each of these experiments show that data obfuscated by DIOD may be utilized in the place of real data for a variety of condition monitoring scenarios with no loss in performance.

A Novel Electronic Nose Using Biomimetic Spiking Neural Network for Mixed Gas Recognition

Odors existing in natural environment are typically mixtures of a large variety of chemical compounds in specific proportions. It is a challenging task for an electronic nose to recognize the gas mixtures. Most current research is based on the overall response of sensors and uses relatively simple datasets, which cannot be used for complex mixtures or rapid monitoring scenarios. In this study, a novel electronic nose (E-nose) using a spiking neural network (SNN) model was proposed for the detection and recognition of gas mixtures. The electronic nose integrates six commercial metal oxide sensors for automated gas acquisition. SNN with a simple three-layer structure was introduced to extract transient dynamic information and estimate concentration rapidly. Then, a dataset of mixed gases with different orders of magnitude was established by the E-nose to verify the model’s performance. Additionally, random forests and the decision tree regression model were used for comparison with the SNN-based model. Results show that the model utilizes the dynamic characteristics of the sensors, achieving smaller mean squared error (MSE < 0.01) and mean absolute error (MAE) with less data compared to random forest and decision tree algorithms. In conclusion, the electronic nose system combined with the bionic model shows a high performance in identifying gas mixtures, which has a great potential to be used for indoor air quality monitoring in practical applications.