Search Results (217)

7,7′,8,8′-tetracyanoquinodimethane (TCNQ) is one of the most widely used effective surface electron acceptors in organic electronics and sensors, which opens up a very interesting field in electrochemical applications. In this review article, we outline the historical context of electrochemically stable selective electrode materials based on TCNQ and its derivatives and their development, their electrochemical characteristics, and the experimental aspects of their electrochemical applications. TCNQ-modified electrodes are characterized by long-term stability, reproducibility, and a low detection limit compared to other sensors; thus, their use can increase determination speed and flexibility and reduce investigation costs. TCNQ and its derivatives can also be successfully combined with other detector materials for cancer-related clinical diagnostic testing. Examples of simple, rapid, and sensitive detection procedures for various analytes are provided. Applications of new electrochemically stable TCNQ-based metal/covalent–organic hybrid frameworks, with exceptionally large surface areas, tunable pore sizes, diverse functionality, and high electrical conductivity, are also presented. As a result, they also offer enormous potential as revolutionary catalysts, drug carrier systems, and smart materials, as well as for use in gas storage. The use of TCNQ compounds as promising active electrode materials in high-power organic batteries/energy storage devices is discussed. We hope that the information featured in this review will provide readers with a good understanding of the chemistry of TCNQ and, more importantly, help to find good ways to prepare new micro-/nanoelectrode materials for rational sensor design. Full article

Index modulation (IM) is considered a promising approach for fifth-generation wireless systems due to its spectral efficiency and reduced complexity compared to conventional modulation techniques. However, IM faces difficulties in environments with unpredictable channel conditions, particularly in accurately detecting index values and dynamically adjusting index assignments. Deep learning (DL) offers a potential solution by improving detection performance and resilience through the learning of intricate patterns in varying channel conditions. In this paper, we introduce a robust detection method based on a hybrid DL (HDL) model designed specifically for orthogonal frequency-division multiplexing with IM (OFDM-IM) in challenging channel environments. Our proposed HDL detector leverages a one-dimensional convolutional neural network (1D-CNN) for feature extraction, followed by a bidirectional long short-term memory (Bi-LSTM) network to capture temporal dependencies. Before feeding data into the network, the channel matrix and received signals are preprocessed using domain-specific knowledge. We evaluate the bit error rate (BER) performance of the proposed model using different optimizers and equalizers, then compare it with other models. Moreover, we evaluate the throughput and spectral efficiency across varying SNR levels. Simulation results demonstrate that the proposed hybrid detector surpasses traditional and other DL-based detectors in terms of performance, underscoring its effectiveness for OFDM-IM under uncertain channel conditions. Full article

The silicon carbide (SiC) Schottky diode (SBD) detector in a SiC hybrid photomultiplier tube (HPMT) generates signals by receiving photocathode electrons with an energy of 10 keV. So, the performance of the SiC SBD under electron irradiation with an energy of 10 keV has an important significance for the application of the SiC-HPMT. However, studies on 10 keV radiation effects on the SiC SBDs were rarely reported. In this paper, the performance degradation of the SiC SBDs irradiated by 10 keV electrons at different fluences was investigated. After the irradiation, the forward current of the SiC SBDs increased, and the turn-on voltage decreased with the irradiation fluences until 1.6 × 10¹⁶ cm⁻². According to the capacitance–voltage (C-V) curves, the effective doping concentration increased slightly after the irradiation, and an obvious discrepancy of C-V curves occurred below 5 V. Moreover, as a radiation detector, the peak position of the α-particles’ amplitude spectrum changed slightly, and the energy resolution was also slightly reduced after the irradiation due to the high collection charge efficiency (CCE) still being larger than 99.5%. In addition, the time response of the SiC SBD to the 50 ns pulsed X-ray was almost not affected by the irradiation. The results indicated that the performance degradation of the SiC SBD irradiated at the fluence of 1.5 × 10¹⁷ cm⁻² would not result in a deterioration of the properties of the SiC-HPMT and showed an important significance for the supplement of the radiation resistance of the SiC SBD radiation detector. Full article

In the realm of smart grids, machine learning (ML) detectors—both binary (or supervised) and anomaly (or unsupervised)—have proven effective in detecting electricity theft (ET). However, binary detectors are designed for specific attacks, making their performance unpredictable against new attacks. Anomaly detectors, conversely, are trained on benign data and identify deviations from benign patterns as anomalies, but their performance is highly sensitive to the selected threshold values. Additionally, ML detectors are vulnerable to evasion attacks, where attackers make minimal changes to malicious samples to evade detection. To address these limitations, we introduce a hybrid anomaly detector that combines a Deep Auto-Encoder (DAE) with a One-Class Support Vector Machine (OCSVM). This detector not only enhances classification performance but also mitigates the threshold sensitivity of the DAE. Furthermore, we evaluate the vulnerability of this detector to benchmark evasion attacks. Lastly, we propose an accurate and robust cluster-based DAE+OCSVM ET anomaly detector, trained using Explainable Artificial Intelligence (XAI) explanations generated by the Shapley Additive Explanations (SHAP) method on consumption readings. Our experimental results demonstrate that the proposed XAI-based detector achieves superior classification performance and exhibits enhanced robustness against various evasion attacks, including gradient-based and optimization-based methods, under a black-box threat model. Full article

During the development of the Chengdu–Chongqing Urban Agglomeration (CCUA) in China, PM_2.5 pollution severely threatened public health, presenting a significant environmental challenge. This study employs a novel spatial interpolation method known as High Accuracy Surface Modeling (HASM), along with the geographical detector method, local and regional contributions calculation model, and the Hybrid Single–Particle Lagrangian Integrated Trajectory model to analyze the seasonal spatial distribution of PM_2.5 concentrations and their anthropogenic driving factors from 2014 to 2023. The transport pathway and potential sources of seasonal PM_2.5 concentrations were also examined. The results showed the following: (1) HASM was identified as the most suitable interpolation method for monitoring PM_2.5 concentrations in the CCUA; (2) The PM_2.5 concentrations exhibited a decreasing trend across all seasons, with the highest values in winter and the lowest in summer. Spatially, the concentrations showed a pattern of being higher in the southwest and lower in the southeast; (3) Industrial soot (dust) emissions (ISEs) and industry structure (IS) were the most important anthropogenic driving factors influencing PM_2.5 pollution; (4) The border area between the eastern part of the Tibet Autonomous Region and western Sichuan province in China significantly contribute to PM_2.5 pollution in the CCUA, especially during winter. Full article

In this paper, we study covert communications in a hybrid decode-and-forward (DF)/ amplify-and-forward (AF) relay system. The considered relay in normal operation forwards messages from a source node to a destination node in either DF or AF mode on request. Meanwhile, the source and destination nodes also attempt to secretly exchange covert messages such as confidential or sensitive information and avoid detection by the covert message detector embedded on the relay. We first establish an optimal DF/AF mode selection criterion to maximize the covert rate based on the analyses of delay-aware achievable covert rates of individual DF and AF modes. To further reduce the time complexity, we propose a low-complexity selection criterion as well for practical use. The numerical results demonstrate the covert rate gain as high as 50% and running time gain as high as 20% for particular system parameters, which verify the effectiveness of the proposed criteria. Full article

Optical remote sensing images often feature high resolution, dense target distribution, and uneven target sizes, while transformer-based detectors like DETR reduce manually designed components, DETR does not support arbitrary-oriented object detection and suffers from high computational costs and slow convergence when handling large sequences of images. Additionally, bipartite graph matching and the limit on the number of queries result in transformer-based detectors performing poorly in scenarios with multiple objects and small object sizes. We propose an improved DETR detector for Oriented remote sensing object detection with Feature Reconstruction and Dynamic Query, termed DETR-ORD. It introduces rotation into the transformer architecture for oriented object detection, reduces computational cost with a hybrid encoder, and includes an IFR (image feature reconstruction) module to address the loss of positional information due to the flattening operation. It also uses ATSS to select auxiliary dynamic training queries for the decoder. This improved DETR-based detector enhances detection performance in challenging oriented optical remote sensing scenarios with similar backbone network parameters. Our approach achieves superior results on most optical remote sensing datasets, such as DOTA-v1.5 (72.07% mAP) and DIOR-R (66.60% mAP), surpassing the baseline detector. Full article

This paper presents a novel hybrid approach to feature detection designed specifically for enhancing Feature-Based Image Registration (FBIR). Through an extensive evaluation involving state-of-the-art feature detectors such as BRISK, FAST, ORB, Harris, MinEigen, and MSER, the proposed hybrid detector demonstrates superior performance in terms of keypoint detection accuracy and computational efficiency. Three image acquisition methods (i.e., rotation, scene-to-model, and scaling transformations) are considered in the comparison. Applied across a diverse set of remote-sensing images, the proposed hybrid approach has shown marked improvements in match points and match rates, proving its effectiveness in handling varied and complex imaging conditions typical in satellite and aerial imagery. The experimental results have consistently indicated that the hybrid detector outperforms conventional methods, establishing it as a valuable tool for advanced image registration tasks. Full article

Semiconductor photodetectors can work only in specific material-dependent light wavelength ranges, connected with the bandgaps and absorption capabilities of the utilized semiconductors. This limitation has driven the development of hybrid devices that exceed the capabilities of individual materials. In this study, for the first time, a hybrid heterojunction photodetector based on methylammonium lead bromide (MAPbBr₃) polycrystalline film deposited on gallium arsenide (GaAs) was presented, along with comprehensive morphological, structural, optical, and photoelectrical investigations. The MAPbBr₃/GaAs heterojunction photodetector exhibited wide spectral responsivity, from 540 to 900 nm. The fabrication steps of the prototype device, including a new preparation recipe for the MAPbBr₃ solution and spinning, will be disclosed and discussed. It will be shown that extending the soaking time and refining the precursor solution’s stoichiometry may enhance surface coverage, adhesion to the GaAs, and film uniformity, as well as provide a new way to integrate MAPbBr₃ on GaAs. Compared to the pristine MAPbBr₃, the enhanced structural purity of the perovskite on GaAs was confirmed by X-ray Diffraction (XRD) upon optimization compared to the conventional glass substrate. Scanning Electron Microscopy (SEM) revealed the formation of microcube-like structures on the top of an otherwise continuous MAPbBr₃ polycrystalline film, with increased grain size and reduced grain boundary effects pointed by Energy-Dispersive Spectroscopy (EDS) and cathodoluminescence (CL). Enhanced absorption was demonstrated in the visible range and broadened photoluminescence (PL) emission at room temperature, with traces of reduction in the orthorhombic tilting revealed by temperature-dependent PL. A reduced average carrier lifetime was reduced to 13.8 ns, revealed by time-resolved PL (TRPL). The dark current was typically around 8.8 × 10⁻⁸ A. Broad photoresponsivity between 540 and 875 nm reached a maximum of 3 mA/W and 16 mA/W, corresponding to a detectivity of 6 × 10¹⁰ and 1 × 10¹¹ Jones at −1 V and 50 V, respectively. In case of on/off measurements, the rise and fall times were 0.40 s and 0.61 s or 0.62 s and 0.89 s for illumination, with 500 nm or 875 nm photons, respectively. A long-term stability test at room temperature in air confirmed the optical and structural stability of the proposed hybrid structure. This work provides insights into the physical mechanisms of new hybrid junctions for high-performance photodetectors. Full article

For the first time, RGB and multispectral sensors deployed on UAVs were used to facilitate grave detection in a desert location. The research sought to monitor surface anomalies caused by burials using manual and enhanced detection methods, which was possible up to 18 months. Near-IR (NIR) and Red-Edge bands were the most suitable for manual detection, with a 69% and 31% success rate, respectively. Meanwhile, the enhanced method results varied depending on the sensor. The standard Reed–Xiaoli Detector (RXD) algorithm and Uniform Target Detector (UTD) algorithm were the most suitable for RGB data, with 56% and 43% detection rates, respectively. For the multispectral data, the percentages varied between the algorithms with a hybrid of the RXD and UTD algorithms yielding a 56% detection rate, the UTD algorithm 31%, and the RXD algorithm 13%. Moreover, the research explored identifying grave mounds using the normalized digital surface model (nDSM) and evaluated using the normalized difference vegetation index (NDVI) in grave detection. nDSM successfully located grave mounds at heights as low as 1 cm. A noticeable difference in NDVI values was observed between the graves and their surroundings, regardless of the extreme weather conditions. The results support the potential of using RGB and multispectral sensors mounted on UAVs for detecting burial sites in an arid environment. Full article

In recent years, research on adversarial attack techniques for remote sensing object detection (RSOD) has made great progress. Still, most of the research nowadays is on end-to-end attacks, which mainly design adversarial perturbations based on the prediction information of the object detectors (ODs) to achieve the attack. These methods do not discover the common vulnerabilities of the ODs and, thus, the transferability is weak. Based on this, this paper proposes a foreground feature approximation (FFA) method to generate adversarial examples (AEs) that discover the common vulnerabilities of the ODs by changing the feature information carried by the image itself to implement the attack. Specifically, firstly, the high-quality predictions are filtered as attacked objects using the detector, after which a hybrid image without any target is made, and the hybrid foreground is created based on the attacked targets. The images’ shallow features are extracted using the backbone network, and the features of the input foreground are approximated towards the hybrid foreground to implement the attack. In contrast, the model predictions are used to assist in realizing the attack. In addition, we have found the effectiveness of FFA for targeted attacks, and replacing the hybrid foreground with the targeted foreground can realize targeted attacks. Extensive experiments are conducted on the remote sensing target detection datasets DOTA and UCAS-AOD with seven rotating target detectors. The results show that the mAP of FFA under the IoU threshold of 0.5 untargeted attack is 3.4% lower than that of the advanced method, and the mAP of FFA under targeted attack is 1.9% lower than that of the advanced process. Full article

Object detection is becoming increasingly critical in autonomous driving. However, the accuracy and effectiveness of object detectors are often constrained by the obscuration of object features and details in adverse weather conditions. Therefore, this paper presented the DAN-YOLO vehicle object detector specifically designed for driving conditions in adverse weather. Building on the YOLOv7-Tiny network, SPP was replaced with SPPF, resulting in the SPPFCSPC structure, which enhances processing speed. The concept of Hybrid Dilated Convolution (HDC) was also introduced to improve the SPPFCSPC and ELAN-T structures, expanding the network’s receptive field (RF) while maintaining a lightweight design. Furthermore, an efficient multi-scale attention (EMA) mechanism was introduced to enhance the effectiveness of feature fusion. Finally, the Wise-IoUv1 loss function was employed as a replacement for CIoU to enhance the localization accuracy of the bounding box (bbox) and the convergence speed of the model. With an input size of 640 × 640, the DAN-YOLO algorithm proposed in this study achieved an increase in mAP0.5 values of 3.4% and 6.3% compared to the YOLOv7-Tiny algorithm in the BDD100K and DAWN benchmark tests, respectively, while achieving real-time detection (142.86 FPS). When compared with other state-of-the-art detectors, it reports better trade-off in terms of detection accuracy and speed under adverse driving conditions, indicating the suitability for autonomous driving applications. Full article

Trees’ structural defects are responsible for the reduction in forest product quality and the accident of tree collapse under extreme environmental conditions. Although the manual view inspection for assessing tree health condition is reliable, it is inefficient in discriminating, locating, and quantifying the defects with various features (i.e., crack and hole). There is a general need for investigation of efficient ways to assess these defects to enhance the sustainability of trees. In this study, the deep learning algorithms of lightweight You Only Look Once (YOLO) and encoder-decoder network named DeepLabv3+ are combined in unmanned aerial vehicle (UAV) observations to evaluate trees’ structural defects. Experimentally, we found that the state-of-the-art detector YOLOv7-tiny offers real-time (i.e., 50–60 fps) and long-range sensing (i.e., 5 m) of tree defects but has limited capacity to acquire the patterns of defects at the millimeter scale. To address this limitation, we further utilized DeepLabv3+ cascaded with different network architectures of ResNet18, ResNet50, Xception, and MobileNetv2 to obtain the actual morphology of defects through close-range and pixel-wise image semantic segmentation. Moreover, the proposed hybrid scheme YOLOv7-tiny_DeepLabv3+_UAV assesses tree’s defect size with an averaged accuracy of 92.62% (±6%). Full article

Modeling the response of gamma detectors has long been a challenge within the nuclear community. Significant research has been conducted to digitally replicate instruments that can cost over USD 100,000 and are difficult to operate outside of a laboratory setting. The large cost and availability prevent some from making use of such equipment. Subsequently, there have been multiple attempts to create cost-effective codes that replicate the response of sodium-iodide and high-purity germanium detectors for data derivation related to gamma-ray interaction with matter. While robust programs do exist, they are often subject to export controls and/or they are not intuitive to use. Through the use of hybrid Monte Carlo methods, MATLAB can be used to produce a fast first-order response of various gamma-ray detectors. The combination of a graphical user interface with a numerical-based script allows for open-source and intuitive code. When benchmarked with experimental data from Co-60, Cs-137, and Na-22, the code can numerically calculate a response comparable to experimental and industry-standard response codes. Evidence supports both savings in computational requirements and the inclusion of an intuitive user experience that does not heavily compromise data when compared to other standard codes, such as MCNP and GADRAS, or experimental results. When the application is installed on a Dell Intel i7 computer with 16 cores, the average time to simulate the benchmarked isotopes is 0.26 s. Installation on an HP Intel i7 four-core machine runs the same isotopes in 1.63 s. The results indicate that simple gamma detectors can be modeled in an open-source format. The anticipation for the MATLAB application is to be a tool that can be easily accessible and provide datasets for use in an academic setting requiring gamma-ray detectors. Ultimately, this article provides evidence that hybrid Monte Carlo codes in an open-source format can benefit the nuclear community in both computational time and up-front cost for access. Full article

Arabic handwriting recognition and conversion are crucial for financial operations, particularly for processing handwritten amounts on cheques and financial documents. Compared to other languages, research in this area is relatively limited, especially concerning Arabic. This study introduces an innovative AI-driven method for simultaneously recognizing and converting Arabic handwritten legal amounts into numerical courtesy forms. The framework consists of four key stages. First, a new dataset of Arabic legal amounts in handwritten form (“.png” image format) is collected and labeled by natives. Second, a YOLO-based AI detector extracts individual legal amount words from the entire input sentence images. Third, a robust hybrid classification model is developed, sequentially combining ensemble Convolutional Neural Networks (CNNs) with a Vision Transformer (ViT) to improve the prediction accuracy of single Arabic words. Finally, a novel conversion algorithm transforms the predicted Arabic legal amounts into digital courtesy forms. The framework’s performance is fine-tuned and assessed using 5-fold cross-validation tests on the proposed novel dataset, achieving a word level detection accuracy of 98.6% and a recognition accuracy of 99.02% at the classification stage. The conversion process yields an overall accuracy of 90%, with an inference time of 4.5 s per sentence image. These results demonstrate promising potential for practical implementation in diverse Arabic financial systems. Full article