Fast Detection Research Articles

On-Chip Annealing Using Embedded Micro-Heater for Highly Sensitive and Selective Gas Detection.

The demand for gas sensing systems that enable fast and precise gas recognition is growing rapidly. However, substantial challenges arise from the complex fabrication process of sensor arrays, time-consuming data transmission to an external processor, and high energy consumption in multi-stage data processing. In this study, a gas sensing system using on-chip annealing for fast and power-efficient gas detection is proposed. By utilizing a micro-heater embedded in the gas sensor, the sensing material of adjacent sensors in the same substrate can be easily varied without further fabrication steps. The response to oxidizing gas is constrained in metal oxide (MOX) sensing material with small grain sizes, as the depletion width of grain cannot extend beyond the grain size during the gas reaction. On the other hand, the response to reducing gases and humidity, which decrease the depletion width, is less affected by grain sizes. A readout circuit integrating a differential amplifier and dual FET-type gas sensors effectively emphasizes the response to oxidizing gases by canceling the response to reducing gases and humidity. The selective on-chip annealing method is applicable to various MOX sensing materials, demonstrating its potential for application in commercial fields due to its simplicity and expandability.

ДОСЛІДЖЕННЯ КОНТЕКСТНО-ЧУТЛИВОГО АЛГОРИТМУ МОНІТОРИНГУ КІБЕРБЕЗПЕКИ НА ОСНОВІ РЕКУРЕНТНИХ НЕЙРОННИХ МЕРЕЖ

The most common problems faced by modern information and communication systems (ICS) in the context of combating cyber threats were examined in the paper. The importance of ensuring the reliable operation of ICS, and protecting their users' private data from unauthorized interception or destruction was emphasized. The main principles of effective protection of ICS systems against possible interference in their work were defined. The classification of cyber threats and their impact on the functioning of information systems was presented. Features of the use of modern information technologies were determined, such as machine learning (ML), and recurrent neural networks (RNN) for increasing the effectiveness of detecting and preventing such threats, speeding up the process of calculating large volumes of information about various aspects of the work of information and communication systems. The parameters of the analysis of ICS behavior, which indicate the presence of problems in cyber security, were studied. The features and advantages of deploying RNN in ICS were analyzed, which makes it possible to simplify the tasks of cyber defense. A modified context-sensitive algorithm for cyber security monitoring (CCM-RNN) was proposed, which is based on RNN and allows taking into account the dynamics of system changes in the established context, for example, the type or volume of traffic from users, etc. The method of selecting the most effective parameters and properties of ICS for detecting cyber threats was improved. The results of the study of the effectiveness of the use of the modified CCM-RNN algorithm demonstrated its broad capabilities for fast and accurate detection of anomalies in the operation of ICs that may threaten their cyber security. By changing the number of properties of the CCM-RNN algorithm, which correspond to the characteristics of various aspects of the IC, it is possible to achieve the maximum accuracy of cyber threat detection. The modified algorithm also allows for the reduction of the duration of calculations during analysis. Based on the research results, a conclusion was made about the feasibility of using the proposed modified CCM-RNN algorithm for the ability to detect cyber security threats in ICS by flexibly adjusting the number and type of learning parameters of neural networks. In this way, the accuracy and duration of calculations were optimized, as well as the peculiarities and contexts of information and communication systems were taken into account.

Vehicle Lane Assist System

This research paper presents the development of a fast and reliable lane detection device designed to detect and alert the user for efficient and safe journey .Our lane detection device makes it easier to see lane markings on the road and warns the user when he switches to wrong side of road. This budget friendly device will provide the real time data with the help of sensors and used it for google maps integration. Our device is even flexible foe extreme weather conditions.

A Portable Readout System for Biomarker Detection with Aptamer-Modified CMOS ISFET Array

Biosensors based on ion-sensitive field effect transistors (ISFETs) combined with aptamers offer a promising and convenient solution for point-of-care testing applications due to the ability for fast and label-free detection of a wide range of biomarkers. Mobile and easy-to-use readout devices for the ISFET aptasensors would contribute to further development of the field. In this paper, the development of a portable PC-controlled device for detecting aptamer-target interactions using ISFETs is described. The device assembly allows selective modification of individual ISFETs with different oligonucleotides. Ta2O5-gated ISFET structures were optimized to minimize trapped charge and capacitive attenuation. Integrated CMOS readout circuits with linear transfer function were used to minimize the distortion of the original ISFET signal. An external analog signal digitizer with constant voltage and superimposed high-frequency sine wave reference voltage capabilities was designed to increase sensitivity when reading ISFET signals. The device performance was demonstrated with the aptamer-driven detection of troponin I in both reference voltage setting modes. The sine wave reference voltage measurement method reduced the level of drift over time and enabled a lowering of the minimum detectable analyte concentration. In this mode (constant voltage 2.4 V and 10 kHz 0.1Vp-p), the device allowed the detection of troponin I with a limit of detection of 3.27 ng/mL. Discrimination of acute myocardial infarction was demonstrated with the developed device. The ISFET device provides a platform for the multiplexed detection of different biomarkers in point-of-care testing.

Operation of a 250μm-thick SiC detector with DT neutrons at high temperatures

The Silicon Carbide detector (SiC) is an object of research as an alternative to diamond detectors for fast neutron detection and spectrometry where harsh environments are an issue, like in Tokamaks. Since future breeding blankets mock-ups will feature temperatures up to 550 °C, diamond detectors were characterized in the past, finding limitations in their functionality at high temperatures. This paper expands on the previous work by proving the detection of fast neutrons with good detection parameters of a 250 μm-thick 4 H-SiC detector prototype at temperatures up to 250 °C, highlighting the detector's excellent resilience to temperature. The experiment is conducted with instrumentation similar to the one used in the past with diamond detectors, using as source of irradiation the Frascati Neutron Generator (FNG) in ENEA, which is accelerator driven neutron source based on deuterium-tritium (DT) fusion reaction.

FCS-YOLO: an efficient algorithm for detecting steel surface defects

Due to the imperfection of the manufacturing process and external factors, there may be some defects on the steel surface, which seriously affects the life and availability of the steel. Aiming at the problems of low precision and poor real-time performance in the inspection of steel surface defects in the past, an algorithm named FCS-you only look once (YOLO) was proposed for the inspection of steel surface defects. Firstly, the backbone feature extraction network is reconstructed using FasterNet to reduce the number of model parameters and weight size. Secondly, a new C2f-global attention mechanism module is proposed, which can focus image features more accurately by dynamically adjusting feature response. Finally, the attention mechanism SK is embedded in the feature extraction network to adjust the receptive field of the model, strengthen its feature recognition ability, and effectively improve the detection accuracy of steel surface defects. The test results show that FCS-YOLO has the best detection effect and the fastest detection speed among the comparison models. On the NEU-DET Dataset, the detection accuracy reached 81.8% and the detection speed reached 129.87 frames per second, which met the requirements of accurate and fast detection of strip surface defects.

The Development of a Specific Nanofiber Bioreceptor for Detection of Escherichia coli and Staphylococcus aureus from Air

Polluted air and the presence of numerous airborne pathogens affect our daily lives. The sensitive and fast detection of pollutants and pathogens is crucial for environmental monitoring and effective medical diagnostics. Compared to conventional detection methods (PCR, ELISA, metabolic tests, etc.), biosensors bring a very attractive possibility to detect chemicals and organic particles with the mentioned reliability and sensitivity in real time. Moreover, by integrating nanomaterials into the biosensor structure, it is possible to increase the sensitivity and specificity of the device significantly. However, air quality monitoring could be more problematic even with such devices. The greatest challenge with conservative and sensing methods for detecting organic matter such as bacteria is the need to use liquid samples, which slows down the detection procedure and makes it more difficult. In this work, we present the development of a polyacrylonitrile nanofiber bioreceptor functionalized with antibodies against bacterial antigens for the specific interception of bacterial cells directly from the air. We tested the presented novel nanofiber bioreceptor using a unique air filtration system we had previously created. The prepared antibody-functionalized nanofiber membranes for air filtration and pathogen detection (with model organisms E. coli and S. aureus) show a statistically significant increase in bacterial interception compared to unmodified nanofibers. Creating such a bioreceptor could lead to the development of an inexpensive, fast, sensitive, and incredibly selective bionanosensor for detecting bacterial polluted air in commercial premises or medical facilities.

Research on application of multimodal data fusion in intelligent building environment perception

With the rapid development of the building industry, intelligent buildings benefit from its safety, energy saving, environmental protection and integration and other advantages have been widely loved by people, most operators also realize the importance of intelligent buildings to bring people humanized and customized services, and in order to realize the personalized service of the building, multi-modal data fusion is an effective method. On the other hand, in today’s Internet of Things society, many practical applications need to deploy a large number of sensing equipment for data collection and processing, so as to carry out high-quality monitoring of the physical world, but due to the inherent limitations of these hardware equipment and the influence of factors such as the environment, single mode data often cannot be completely and comprehensively monitored to the physical world’s changing characteristics. In this development context, multi-modal data fusion has become a research hotspot in the field of machine learning. Based on this, this paper proposes a one-stage fast object detection model with multi-level fusion of multi-modal features and end-to-end characteristics for building indoor environment perception, and conducts experimental analysis on the performance of the model. The verification results show that the accuracy of the proposed method is 50.7% and the running speed is 0.107 s, which has better performance than the existing detection methods.

Fast bus voltage detection method for single-phase power converters with split capacitors based on reduced-order generalized integrals

Fast bus voltage detection method for single-phase power converters with split capacitors based on reduced-order generalized integrals

Quaternary conducting Cs/GO/PANi hydrogel composites: A smart material for room temperature hydrogen sensing

Hydrogen sensors are essential for hydrogen energy technology development and safety monitoring. However, hydrogen sensing is difficult because it requires effective and fast detection of tiny quantities of hydrogen. In situ oxidative radical polymerization is used in this study to combine polyaniline (PANi) with a chitosan/graphene oxide (Cs/GO) hydrogel, which is very good at conducting electricity, to make it sensor. FTIR, XRD, SEM, and UV were used to evaluate the material's functional groups, crystal structure, morphology, and bandgap. This work shows that Cs/GO/PANi can synthesise a hydrogel-based sensor. Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and chronoamperometry (CA) were used to evaluate electron transfer mobility, redox behaviour, and hydrogen sensing. Using a hydrogen source with a concentration of 0.5–2.5 M KOH, the hydrogen sensor's response time, recovery sensitivity, and limit of detection were found to be 0.4 s, 0.8 s, 54.3748 μAM, and 3.5520 μM, respectively. Under conventional conditions, the experiment demonstrated significant sensing capabilities at ambient temperature. According to the research, the sensor works so well because of the π-π stacking interaction, the irregular structure, and the way the GO-based hydrogel and PANi work together.

Young Children of Mothers with a History of Depression Show Attention Bias to Sad Faces: An Eye-tracking Study.

Maternal depression is a predictor of the emergence of depression in the offspring. Attention bias (AB) to negative emotional stimuli in children may serve as a risk factor for children of depressed parents. The present study aimed to examine the effect of maternal major depressive disorder (MDD) history on AB to emotional faces in children at age four, before the age of onset for full-blown psychiatric symptoms. The study also compared AB patterns between mothers and their offspring. Fifty-eight mothers and their four-year-old children participated in this study, of which 27 high-risk (HR) children had mothers with MDD during their children's lifetime. Attention to emotional faces was measured in both children and their mothers using an eye-tracking visual search task. HR children exhibited faster detection and longer dwell time toward the sad than happy target faces. The low-risk (LR) children also displayed a sad bias but to a lesser degree. Children across both groups showed AB towards angry target faces, likely reflecting a normative AB pattern. Our findings indicate that AB to sad faces may serve as an early marker of depression risk. However, we provided limited support for the mother-child association of AB. Future research is needed to examine the longitudinal intergenerational transmission of AB related to depression and possible mechanisms underlying the emergence of AB in offspring of depressed parents.

Rapid and specific detection of Pentastiridius leporinus by recombinase polymerase amplification assay.

Pentastiridius leporinus (Hemiptera: Cixiidae) is the main vector of an emerging and fast spreading sugar beet disease, the syndrome 'basses richesses' (SBR), in different European countries. The disease is caused by the γ-3-proteobacterium 'Candidatus Arsenophonus phytopathogenicus' and the phytoplasma 'Candidatus Phytoplasma solani' which are exclusively transmitted by planthoppers and can lead to a significant loss of sugar content and yield. Monitoring of this insect vector is important for disease management. However, the morphological identification is time consuming and challenging as two additional cixiid species Reptalus quinquecostatus and Hyalesthes obsoletus with a very close morphology have been reported in sugar beet fields. Further, identification of females and nymphs of P. leporinus at species level based on taxonomic key is not possible. In this study, an isothermal nucleic acid amplification based on recombinase polymerase amplification (RPA) was developed to specifically detect P. leporinus. In addition, real-time RPA was developed to detect both adults (male and female) and nymph stages using pure or crude nucleic acid extracts. The sensitivity of the real-time RPA for detection of P. leporinus was comparable to real-time PCR, but a shorter time (< 7 min) was required. This is a first report for real-time RPA application for P. leporinus detection using crude nucleic acid templates which can be applied for fast and specific detection of this vector in the field.

Fast detection of sodium dithionite in sugar using a xanthylium-based fluorescent probe

Dithionite remained in the foodstuff may pose a great threat to the health of consumers. Three xanthylium-based probes were synthesized and their responses to dithionite were explored. Probe SH-1 could respond to dithionite selectively in PBS buffer (15% DMSO, 10 mM, pH = 7.4). Upon the addition of dithionite, the fluorescent emission of SH-1 at 684 nm dropped quickly (within 10 s) and the fluorescence decline was proportional to the concentration of dithionite (0–7.0 μM). The limit of detection was determined to be 0.139 μM. Then, the sensing mechanism was tentatively presented and the structure of resulted adduct (SH-1-SO3−) which was the reaction product of SH-1 and dithionite via a Micheal addition reaction followed by an oxidation reaction was verified. Moreover, white granulated sugar was subjected to the standard spike experiments and the results demonstrated a great potential of SH-1 for the quantitative monitoring of dithionite in foodstuffs.

Improvements of magnetic nanoparticle assays for SARS-CoV-2 detection using a mimic virus approach

Immunoassays with magnetic nanoparticles (MNPs) as markers are a promising approach for the fast and sensitive virus detection. Upon binding of antibody-functionalized MNP on virus proteins, the hydrodynamic diameter increases and a change in the Brownian relaxation time can be measured. In this study, we detect the whole SARS-CoV-2 by mimicking it with streptavidin-coated polystyrene beads with biotinylated spike proteins. Changes of the MNP dynamics are measured by alternating current susceptometry and magnetic particle spectroscopy. Due to the multiple binding sites of MNP and virus, crosslinking enlarges the change of the hydrodynamic diameter. In order to improve the sensitivity and the limit of detection of the assay, the ratio of the virus to the MNP amount RMV/MNP is investigated in detail. High RMV/MNP ratios lead to a saturation of the MNPs with viruses, so that the cluster size and therefore the sensitivity decrease again. Additionally, it is found that the smallest virus concentrations can be detected for small MNP concentrations. It is also shown that the RMV/MNP range that can be used for an unambiguous detection of viruses depends on the virus/MNP concentration; it shifts with increasing MNP concentration to smaller RMV/MNP values. For very small virus concentrations, an increase of the Brownian relaxation time is detected implying a decrease of the hydrodynamic diameter. Furthermore, the optimal antibody concentration for MNP functionalization was determined. It is also found that a washing process with a centrifuge improves the sensitivity by reliably removing unbound antibodies and eliminating small MNPs with improper functionalization.

An investigation of Inonotus obliquus in Estonia: Its distribution, abundance of conks, rot extent in infected trees, and peculiarities of basidiospore dispersal

Inonotus obliquus, a well-known forest fungal pathogen, started gaining attention for its potential medicinal uses. Known as Chaga, the fungus has become prominent due to the bioactive compounds present in its conks. Forest owners are cultivating it to produce conks as a source of non-timber profit. However, not much is known about the natural occurrence of this pathogen, its formation of conks, the extent of decay in the infected trees, and the distribution of its basidiospores. Across 80 forest stands in Estonia dominated by Betula pendula, B. pubescens, Alnus incana, and A. glutinosa, the conks of I. obliquus were documented in 17 stands. The conks were more common in continental Estonia compared to the western islands. Among 800 randomly chosen trees, I. obliquus conks were documented on ten trees. Additionally, an asymptomatic I. obliquus infection was detected in one tree. After two years of spore trapping, I. obliquus basidiospores were detected only on four occasions, between the end of July and the beginning of September, and only at night. The proportion of rot damage in the total volume of model tree wood ranged from 8.6% to 58.5%. For fast and reliable detection of I. obliquus, TaqMan species-specific qPCR primers were developed and tested.

Universal digital high-resolution melting for the detection of pulmonary mold infections.

Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.

Fisher matrix based fault detection for PMUs data in power grids

In this paper, using the data collected from phasor measurement units (PMUs), two methods based on Fisher random matrix are proposed to detect faults in power grids. Firstly, the fault detection matrix is constructed and the event detection problem is reformatted as a testing problem for two-sample covariance matrices, which is related with the so-called Fisher matrix. To save computing resources, the screening step of fault interval based on the test statistic is designed to check the existence of faults. Then two point-by-point methods are proposed to determine the time of the fault in the selected interval. One method detects faults by the limiting spectral distribution of the standard Fisher matrix, which can detect the faults with higher accuracy. The other method tests the faults based on the proposed test statistic , which has a faster detection speed. Compared with existing works, the simulation results illustrate that two methods proposed in this paper cost less computational time and provide a higher degree of accuracy and sensitivity.

Fast Attack Detection for Cyber-Physical Systems Using Dynamic Data Encryption.

To defend the cyber-physical system (CPSs) from cyber-attacks, this work proposes an unified intrusion detection mechanism which is capable to fast hunt various types of attacks. Focusing on securing the data transmission, a novel dynamic data encryption scheme is developed and historical system data is used to dynamically update a secret key involved in the encryption. The core idea of the dynamic data encryption scheme is to establish a dynamic relationship between original data, secret key, ciphertext and its decrypted value, and in particular, this dynamic relationship will be destroyed once an attack occurs, which can be used to detect attacks. Then, based on dynamic data encryption, a unified fast attack detection method is proposed to detect different attacks, including replay, false data injection (FDI), zero-dynamics, and setpoint attacks. Extensive comparison studies are conducted by using the power system and flight control system. It is verified that the proposed method can immediately trigger the alarm as soon as attacks are launched while the conventional χ2 detection could only capture the attacks after the estimation residual goes over the predetermined threshold. Furthermore, the proposed method does not degrade the system performance. Last but not the least, the proposed dynamic encryption scheme turns to normal operation mode as the attacks stop.

Prediction of emission characteristics of diesel/n-hexanol/graphene oxide blended fuels based on fast outlier detection-sparrow search algorithm-bidirectional recurrent neural network

This study aims to utilize deep learning (DL) technology to address the issue of carbon soot emission in traditional internal combustion engines. Spraying and combustion experiments were conducted in a constant volume combustion (CVC) chamber using n-hexanol and graphene oxide nanoparticles as partial substitutes for diesel, with the goal of predicting their emission characteristics based on experimental data. Initially, spray and combustion data under different operating conditions were collected using the CVC experimental setup, and outliers were removed using the fast outlier detection (FOD) algorithm. Subsequently, the learning rate and the number of hidden layer neurons in the Bidirectional Recurrent Neural Network (BiRNN) model were optimized using the Sparrow Search Algorithm (SSA) to enhance the model's accuracy and generalization ability. Unlike previous research methods, the novel FOD-coupled SSA optimization method used in this study ensures the optimal parameter configuration of the speed prediction model while accelerating the convergence speed, which ultimately improves the stability of the model. In addition, the optimal output variables were determined based on the Spearman and Pearson correlation coefficient rules. Finally, the effectiveness of the predictive model was validated using evaluation metrics such as root mean square error (RMSE) and R-squared (R2). The results indicate that the KL coefficient for D80H20GO20 fuel in the CVC device is minimal. The FOD-SSA-BiRNN model achieved R2 values of 0.99409, 0.99529, and 0.99692 for D100, D80H20, and D80H20GO20 fuels, respectively. Therefore, this study provides an effective method for the online accurate prediction of carbon soot emissions in mixed fuels.

Vis-NIR spectroscopic discriminant analysis of aflatoxin B1 excessive standard in peanut meal as feedstuff materials

A fast, simple and reagent-free detection method for aflatoxin B1 (AFB1) is of great significance to food safety and human health. Visible and near-infrared (Vis-NIR) spectroscopy was applied to the discriminant analysis of AFB1 excessive standard of peanut meal as feedstuff materials. Two types of excessive standard discriminant models based on spectral quantitative analysis with partial least squares (PLS) and direct pattern recognition with partial least squares-discrimination analysis (PLS-DA) were established, respectively. Multi-parameter optimization of Norris derivative filtering (NDF) was used for spectral preprocessing; the two-stage wavelength screening method based on equidistant combination-wavelength step-by-step phase-out (EC-WSP) was used for wavelength optimization. A rigorous sample experimental design of calibration-prediction-validation was utilized. The calibration and prediction samples were used for modeling and parameter optimization, and the selected model was validated using the independent validation samples. For quantitative analysis-based, the positive, negative and total recognition-accuracy rates in validation (RARV+, RARV-, and RARV) were 84.8 %, 74.6 % and 79.8 %, respectively; but, the relative root mean square error of prediction was as high as 51.0 %. For pattern recognition-based, the RARV+, RARV-, and RARV were 93.3 %, 90.5 % and 91.9 %, respectively. Moreover, the number of wavelengths N was drastically reduced to 17, and the discrete wavelength combination was in NIR overtone frequency region. The results indicated that, the EC-WSP-PLS-DA model achieved significantly better discrimination effect. Thus demonstrated that Vis-NIR spectroscopy has feasibility for the excessive standard discrimination of aflatoxin B1 in feedstuff materials.