Detection System Research Articles

Design and Development of a Portable Neutron Detection System Based on a 6Li-Doped ZnS(Ag) Scintillator

In this study, the design and development of a portable neutron detection system based on a 6LiF + ZnS(Ag) scintillator was carried out. The detector system was first modeled using the Monte Carlo simulation code MCNP. As a result of the detector simulation, the materials and photomultiplier tubes (PMTs) were supplied for the system, and the material properties and thicknesses were optimized for the best scintillator, with the EJ-420 (Eljen Technology), consisting of 95% 6Li dispersed in ZnS(Ag), chosen because it has a distinctive feature with regard to high thermal neutron detection efficiency. Since this material has been shown to offer a significant advantage in favor of neutron detection in mixed radiation fields with gammas and neutrons, the pulse shape discrimination method was employed. To achieve this, a proper electronic circuit was developed to discriminate the pulses from neutrons and gammas. In the experimental step, the EJ-420 scintillator with a 50-mm diameter was optically coupled with a special fast PMT (Hamamatsu H1949-51 model). In conclusion, this study, which underlines the performed simulations for a neutron detector configuration, gives the obtained experimental results showing discrimination capability in neutron/gamma detection using a 241Am-Be neutron source. The results show that the EJ-420 is a good scintillator due to its highly enriched 6Li transmator, which results in more effective neutron measurements when a portable neutron detector design is chosen.

Validation of a commercially available CAD-system for lung nodule detection and characterization using CT-scans.

This study aims to externally validate a commercially available Computer-Aided Detection (CAD)-system for the automatic detection and characterization of solid, part-solid, and ground-glass lung nodules (LN) on CT scans. This retrospective study encompasses 263 chest CT scans performed between January 2020 and December 2021 at a Dutch university hospital. All scans were read by a radiologist (R1) and compared with the initial radiology report. Conflicting scans were assessed by an adjudicating radiologist (R2). All scans were also processed by CAD. The standalone performance of CAD in terms of sensitivity and false-positive (FP)-rate for detection was calculated together with the sensitivity for characterization, including texture, calcification, speculation, and location. The R1's detection sensitivity was also assessed. A total of 183 true nodules were identified in 121 nodule-containing scans (142 non-nodule-containing scans), of which R1 identified 165/183 (90.2%). CAD detected 149 nodules, of which 12 were not identified by R1, achieving a sensitivity of 149/183 (81.4%) with an FP-rate of 49/121 (0.405). CAD's detection sensitivity for solid, part-solid, and ground-glass LNs was 82/94 (87.2%), 42/47 (89.4%), and 25/42 (59.5%), respectively. The classification accuracy for solid, part-solid, and ground-glass LNs was 81/82 (98.8%), 16/42 (38.1%), and 18/25 (72.0%), respectively. Additionally, CAD demonstrated overall classification accuracies of 137/149 (91.9%), 123/149 (82.6%), and 141/149 (94.6%) for calcification, spiculation, and location, respectively. Although the overall detection rate of this system slightly lags behind that of a radiologist, CAD is capable of detecting different LNs and thereby has the potential to enhance a reader's detection rate. While promising characterization performances are obtained, the tool's performance in terms of texture classification remains a subject of concern. Numerous lung nodule computer-aided detection-systems are commercially available, with some of them solely being externally validated based on their detection performance on solid nodules. We encourage researchers to assess performances by incorporating all relevant characteristics, including part-solid and ground-glass nodules. Few computer-aided detection (CAD) systems are externally validated for automatic detection and characterization of lung nodules. A detection sensitivity of 81.4% and an overall texture classification sensitivity of 77.2% were measured utilizing CAD. CAD has the potential to increase single reader detection rate, however, improvement in texture classification is required.

Hate speech detection in the Bengali language: a comprehensive survey

The detection of hate speech (HS) in online platforms has become extremely important for maintaining a safe and inclusive environment. While significant progress has been made in English-language HS detection, methods for detecting HS in other languages, such as Bengali, have not been explored much like English. In this survey, we outlined the key challenges specific to HS detection in Bengali, including the scarcity of labeled datasets, linguistic nuances, and contextual variations. We also examined different approaches and methodologies employed by researchers to address these challenges, including classical machine learning techniques, ensemble approaches, and more recent deep learning advancements. Furthermore, we explored the performance metrics used for evaluation, including the accuracy, precision, recall, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, specificity, and F1 score, providing insights into the effectiveness of the proposed models. Additionally, we identified the limitations and future directions of research in Bengali HS detection, highlighting the need for larger annotated datasets, cross-lingual transfer learning techniques, and the incorporation of contextual information to improve the detection accuracy. This survey provides a comprehensive overview of the current state-of-the-art HS detection methods used in Bengali text and serves as a valuable resource for researchers and practitioners interested in understanding the advancements, challenges, and opportunities in addressing HS in the Bengali language, ultimately assisting in the creation of reliable and effective online platform detection systems.

A Cooperative Intrusion Detection System for the Internet of Things Using Convolutional Neural Networks and Black Hole Optimization

A Cooperative Intrusion Detection System for the Internet of Things Using Convolutional Neural Networks and Black Hole Optimization

Precise soil coverage in potato planting through plastic film using real-time image recognition with YOLOv4-tiny

Planting potatoes through plastic film with incomplete or excessive soil coverage over seed holes significantly impairs yield. Existing covering methods rely solely on mechanical transmissions, leading to bulky and inconsistent soil coverage of the seed holes. This paper reports an innovative method using a precise soil covering device based on the YOLOv4-tiny real-time object detection system to accurately identify potato plastic film holes and cover them with soil. The system adopts a lightweight and high-precision detection scheme, balancing increased network depth with reduced computation. It can identify holes in the plastic film in real-time and with high accuracy. To verify the effectiveness of YOLOv4-tiny real-time object detection system, a precise soil covering device based on this detection system has been designed and applied to a double crank multi-rod hill-drop planter. Field tests revealed that the system's average accuracy rate for detecting holes is approximately 98%, with an average processing time of 15.15 ms per frame. This fast and accurate performance, combined with the device's robust real-time operation and anti-interference capabilities during soil covering, effectively reduce the problems of soil cover omission and repeated covering caused by existing mechanical transmission methods. The findings reported in this paper are valuable for the development of autonomous potato plastic film precise soil covering devices for commercial use.

Searching for a relationship between the elemental composition of archaeological bones and the occurrence of caries.

Although the macroscopic assessment of dental caries and the assessment of bone elemental composition are quite different, efforts can be made to identify commonalities in the assessment of health and nutritional quality. Both indicators are correlated with dietary habits and are dependent on taphonomic processes occurring in the postmortem substrate. However, teeth exhibit structural resilience of their hard tissues to adverse environmental factors. The aim of the study was to establish a correlation between the elemental composition of bones and the presence of carious lesions. The study material consisted of the following skeletal parts: 161 permanent teeth from 36 individuals and bridge fragments of 36 ribs. The presence of caries was assessed visually using a modified International Caries Detection & Assessment System (ICDAS II) scale. The rib samples were subjected to elemental analysis (zinc (Zn), iron (Fe), magnesium (Mg), calcium (Ca), phosphorus (P), strontium (Sr), barium (Ba)) using spectroscopic methods. The odontological and chemical analyses did not reveal any statistically significant relationships between the Ca/P diagenesis index and dental features. Postmortem tooth loss showed a weak correlation with the diagenesis index. Discoloration, cracks and flaking of the dental crown surfaces may be associated with the intensity of Ca/P diagenesis. However, no significant correlation was found between these phenomena. Among other elements, only Zn levels exhibited a correlation with the caries index.

Genetic algorithm optimized microstructure to enhance waveguide light coupling efficiency at normal incidences

The recent trend to place well-designed photonic structures on waveguides is capable of effectively enhancing waveguides properties. One typical example is a nanostructure-empowered waveguide targeted for efficient light coupling. However, conceiving the high-freedom structures is a time-consuming and labor-intensive process, where an ineffective workflow limits the development of photonic microstructures. To address this issue, we deploy a genetic algorithm to customize structures in order to improve the coupling coefficients under predetermined situations (i.e., normal incidence combined with two polarizations). Three types of micropatterns are first conceived in the periodical model and then fully characterized on the real waveguide sizes. The simulated data reveal that the 550–1650 nm average coupling efficiencies of structure-enabled waveguides are raised by about 2% in contrast to the bare case, and the lineshapes are also flattened thanks to the grating modifications. In short, our solution underlines the role of an algorithm-developed nanostructure to lift waveguide coupling coefficients. By integrating well-engineering patterns, the waveguide-based probes may find a multitude of usages for weak signal detection and communication systems.

A reusable AI-enabled defect detection system for railway using ensembled CNN

A reusable AI-enabled defect detection system for railway using ensembled CNN

Research Paper on Artificial Intelligence

Artificial Intelligence (AI) has emerged as a transformative force across various sectors, revolutionizing processes, enhancing efficiency, and redefining innovation. This research paper delves into the multifaceted landscape of AI, focusing on its applications, knowledge representation, and implications for innovation. The paper begins by exploring the diverse applications of AI across healthcare, gaming, finance, data security, social media, robotics, and e-commerce. In healthcare, AI aids in diagnosis and patient care, while in gaming, it enables strategic game play and enhances user experience. The finance sector leverages AI for automation, analytics, and algorithmic trading, improving decision-making and customer service. AI also plays a vital role in ensuring data security through advanced detection systems, manages vast social media data for enhanced user engagement, and drives innovation in robotics and e-commerce. Moving forward, the paper delves into the realm of expert systems and knowledge representation, elucidating the role of AI in simulating human expertise and modeling complex information structures. It discusses various aspects of knowledge representation, such as propositional knowledge representation, image retrieval, functional relationships between objects, and class representation formalism, highlighting their significance in developing intelligent systems. Furthermore, the paper examines the integration of AI in maintenance practices, both for tangible systems like engineering workshops and intangible products like data extraction wrappers. It underscores the importance of AI in optimizing operational efficiency, reducing downtime, and ensuring continuous data extraction. Lastly, the paper explores the concept of deep learning as a general- purpose invention, discussing its potential implications for innovation, management, institutions, and policy. It addresses key issues such as the management and organization of innovation, intellectual property rights, competition policy, and the cumulative knowledge production facilitated by deep learning. In conclusion, this research paper provides a comprehensive overview of AI's transformative potential, emphasizing the need for further research and analysis to fully comprehend its impact on society, economy, and innovation.

Recent Expansion of the Cascades Volcano Observatory Geophysical Network at Mount Rainier for Improved Volcano and Lahar Monitoring

Abstract The U.S. Geological Survey Cascades Volcano Observatory (CVO) recently expanded its continuous geophysical monitoring at Mount Rainier, an active stratovolcano in Washington state. CVO monitors volcanoes in Oregon, Washington, and Idaho to characterize volcanic systems and detect unrest. Mount Rainier has a history of large lahar occurrences in the Holocene, including at least one that may not have been associated with volcanic activity. Pierce County, Washington, is one of the areas most at risk from large lahars. In the 1990s, CVO collaborated with Pierce County to install the Rainier lahar detection system (RLDS), an automated system designed to detect large lahars in high-risk drainages and mitigate hazards to heavily populated areas. The system was designed to detect lahars within 5–10 min of their occurrence and alert authorities of the need to evacuate populated low-lying areas before lahar arrival. In addition, CVO and the Pacific Northwest Seismic Network (PNSN) maintained and expanded a network of seismic and geodetic monitoring stations on and near the edifice to provide adequate volcano monitoring capabilities. Since 2016, CVO has worked to upgrade the existing RLDS and to expand its capabilities into other drainages around Mount Rainier. This expansion includes installation of 25 new broadband seismic stations with many including infrasound along high-risk drainages, as well as support for equipment upgrades at existing PNSN and CVO volcano monitoring sites. All stations transmit continuous, near-real-time data with dramatically improved spatial coverage for volcano monitoring and lahar hazard mitigation compared to the previous system.

Surface defect visualisation using scanning electromagnetic induction thermography

ABSTRACT Scanning electromagnetic induction thermography (SEIT) has great potential for industrial applications, such as rapid visualising of surface cracks. This dynamic non-destructive testing method has improved detection efficiency but has led to blurred or discontinuous defect boundaries at varying scanning speeds. To alleviate this problem, we explore the use of the patch-based sparse decomposition (PSD) technique. This technique improves dynamic imaging by retaining only the information associated with sparse regression and introducing a locally adaptive threshold. Besides, this method offers a potential thermal image preconditioned scheme for follow-up artificial intelligence detection. Experimental results show that PSD can correct blurred images caused by relative motion, thus improving defect detection accuracy. Moreover, it is suitable for both translational and rotational detection systems. Images reconstructed through the deblurring process demonstrate the ability to visualise holes with a radius of 0.5 mm at velocities up to 150 mm/s, while cracks with a defect width of 0.2 mm on a railroad wheel can be detected at speeds ranging from 25 mm/s to 75 mm/s.

A novel comprehensive method for weak signal blind detection based on adaptive quasi-periodic potential stochastic resonance and its application in bearing fault detection

In order to solve the common output saturation of stochastic resonance systems and the limitation of classical index SNR for blind detection, a novel adaptive quasi-periodic potential stochastic resonance blind detection method is proposed. First, a model of quasi-periodic potential stochastic resonance (QPPSR) possessing infinite steady state is constructed and analyzed for its structure change pattern. The superior performance of the model is verified by using the fourth-order Runge–Kutta algorithm. Secondly, the mechanism of QPPSR is analyzed using the probability flow method, which reveals the relationship between system parameters and performance. Again, a novel comprehensive blind detection index (CBDI) is exquisitely constructed to make up for the shortcomings of each indicator. Finally, CBDI and QPPSR are constructed into an adaptive blind detection system and applied to bearing fault detection. The results analyzed by experiments verify the good engineering application prospect of CBDI-QPPSR.

Design of Inner Ribs with Unequal Stiffness for Deep-Sea Highly Pressure-Resistant Cylindrical Shells and Utilizing NSGA-2 for Lightweight Optimization

For conducting scientific research at depths in the ocean, deep-sea probes are essential pieces of equipment. The cylindrical shell is the most sensible and rational packaging structure for these detectors. New technical challenges for enhancing the pressure resistance and lightweight design of the pressure-resistant cylindrical shell arise from the need to ensure that the detector packaging structure can withstand the immense water pressure at tens of thousands of meters in the underwater environment, while simultaneously reducing the detector packaging structure’s self-weight. This article examines the detection system’s deep-sea pressure-resistant cylindrical shell. To address the issue of the pressure-resistant shell’s insufficient ability to counteract the overall instability caused by the inability to form unstable half-waves in the radial direction when the ring rib pressure-resistant shell experiences it, a design method for the ribs inside the unequal-stiffness pressure-resistant cylindrical shell is suggested. The shell’s instability pressure increases by 9.65 MPa following the stiffness adjustment. Simultaneously, in order to attain even more lightweight optimization, the optimal inner rib section was obtained by applying the orthogonal topology optimization method, which also reduced the weight by 106.8 g and effectively improved the compression stability of the high-pressure cylindrical shell structure. Based on this, key optimization variables were found by performing sensitivity analysis on the cylindrical shell structure’s parameters. Then, with lightweighting as the primary objective, the high-pressure-resistant cylindrical shell’s optimal structural parameters were found using a multi-objective optimization process using the second-generation fast non-dominated genetic algorithm (NSGA-2). This resulted in a weight reduction of 1.2492 kg, or 17.26% of the original pressure-resistant shell. This has led to the development of a lightweight, highly pressure-resistant method for packaging marine exploration equipment structures.

Securing IoT network with hybrid evolutionary lion intrusion detection system: a composite motion optimisation algorithm for feature selection and ensemble classification

ABSTRACT Networks connected to the Internet of Things (IoT) are often vulnerable to attacks. Several existing methods in the intrusion detection system for securing IoT have been presented with ensemble classifier, but it does not accurately classify attack, and also it takes high computation time. With intention of solving the security issues, Intrusion Detection System using Hybrid Evolutionary Lion and Balancing Composite Motion Optimisation Algorithm espoused feature selection with Ensemble Classifier (IDS-IoT-Hybrid ELOA-BCMOA-Ensemble-DT-LSVM-RF-XGBoost) is proposed for Securing IoT Network. At first, data were accumulated from the NSL-KDD data set. Afterward, data is fed to pre-processing, where it restored missing value using mean curvature flow method. At feature selection, optimum features are compiled under Hybrid Evolutionary Lion and Balancing Composite Motion Optimisation Algorithm. Based upon the optimum features, intruders of IoT data are categorised as denial-of-service (DoS), probe, remote to local attack (R2L), user to root attack (U2R), normal (no attack) with the help of Ensemble classifier. Proposed IDS-IoT-Hybrid ELOA-BCMOA-Ensemble-DT-LSVM-RF-XGBoost approach is constructed utilising Python. Then, proposed IDS-IoT-Hybrid ELOA-BCMOA-Ensemble-DT-LSVM-RF-XGBoost approach attains 21.11%, 19.58%, 24.61% and 9.52% higher accuracy; 94.47%, 93.95%, 93.08% and 90.59% lower error rate, and 62.94%, 36.69%, 64.17% and 50.97% less computation time analysed with existing models.

Specific and Sensitive Visual Proviral DNA Detection of Major Pathogenic Avian Leukosis Virus Subgroups Using CRISPR-Associated Nuclease Cas13a

Avian leukosis viruses (ALVs) include a group of avian retroviruses primarily associated with neoplastic diseases in poultry, commonly referred to as avian leukosis. Belonging to different subgroups based on their envelope properties, ALV subgroups A, B, and J (ALV-A, ALV-B, and ALV-J) are the most widespread in poultry populations. Early identification and removal of virus-shedding birds from infected flocks are essential for the ALVs’ eradication. Therefore, the development of rapid, accurate, simple-to-use, and cost effective on-site diagnostic methods for the detection of ALV subgroups is very important. Cas13a, an RNA-guided RNA endonuclease that cleaves target single-stranded RNA, also exhibits non-specific endonuclease activity on any bystander RNA in close proximity. The distinct trans-cleavage activity of Cas13 has been exploited in the molecular diagnosis of multiple pathogens including several viruses. Here, we describe the development and application of a highly sensitive Cas13a-based molecular test for the specific detection of proviral DNA of ALV-A, B, and J subgroups. Prokaryotically expressed LwaCas13a, purified through ion exchange and size-exclusion chromatography, was combined with recombinase polymerase amplification (RPA) and T7 transcription to establish the SHERLOCK (specific high-sensitivity enzymatic reporter unlocking) molecular detection system for the detection of proviral DNA of ALV-A/B/J subgroups. This novel method that needs less sample input with a short turnaround time is based on isothermal detection at 37 °C with a color-based lateral flow readout. The detection limit of the assay for ALV-A/B/J subgroups was 50 copies with no cross reactivity with ALV-C/D/E subgroups and other avian oncogenic viruses such as reticuloendotheliosis virus (REV) and Marek’s disease virus (MDV). The development and evaluation of a highly sensitive and specific visual method of detection of ALV-A/B/J nucleic acids using CRISPR-Cas13a described here will help in ALV detection in eradication programs.

Improved Particle Filter Algorithm for Multi-Target Detection and Tracking

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

Enhanced an Intrusion Detection System for IoT networks through machine learning techniques: an examination utilizing the AWID dataset

Enhanced an Intrusion Detection System for IoT networks through machine learning techniques: an examination utilizing the AWID dataset

Determination of carcinogenic benzo(a)pyrene in heat treated black tea samples from Turkey by using HPLC-fluorescence detection system

Determination of carcinogenic benzo(a)pyrene in heat treated black tea samples from Turkey by using HPLC-fluorescence detection system

Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations.

This study introduces the integration of dynamic computer vision-enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)-based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS2, uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.

A computer vision approach to vehicle detection, classification, and tracking from UAV data for Indian traffic analysis

Traffic surveillance is crucial for road safety and efficiency. This study examines object detection techniques tailored for Indian traffic, highlighting the challenges faced. The aim is to track vehicles, pedestrians, and cyclists using drone-captured data in Indian cities. Urban areas in India grapple with traffic problems due to rapid growth and inadequate infrastructure. Effective monitoring can address these. Recent advancements in object detection, a critical computer vision task, show promise for this application. Employing the YOLOv8 model, trained on our drone-collected dataset, which improved detection accuracy for the Indian Traffic Scenario, is shown. This dataset labelling of vehicle bounding boxes underwent preprocessing using Gaussian Filter, resizing, normalization, and augmentation. Testing showcased the model's real-world applicability in areas like traffic management and autonomous driving. The proposed model thus enhances vehicle detection systems, fostering better safety and decision-making, yielding promising results, as evidenced by 0.86 mAP50 after training and testing the model under own UAV Dataset.