Real Target Research Articles

Fast surface floating organic droplets based dispersive liquid‐liquid microextraction for trace enrichment of multiclass pesticide residues from different fruit juice samples followed by high performance liquid chromatography–diode array detection analysis

AbstractThis study was designed to enable the development of a simple, fast, and environmentally friendly analytical technique utilizing dispersive liquid‐liquid microextraction based on surface floating organic droplets for selective and quantitative enrichment of trace level pesticide contaminants from different fruit juice samples for subsequent detection by high performance liquid chromatography, combined with a diode array detector. The selective extraction was necessitated in order to isolate the seven multiclass pesticide residues frequently occurring in fruit juice samples. The effects of experimental parameters such as pH of sample solution, type and volume of extraction and dispersive solvents, ionic strength and extraction time were optimized. The optimized method was validated using spiked blank sample and satisfactory results for accuracy, with recoveries ranging from 87.23% to 99.45%, with %relative standard deviation between 1.37 and 8.39, precision in terms of %relative standard deviation ≤ 10.78 and linearity at concentration levels from 3 to 1500 ng/ml, which corresponded with correlation coefficients ≥ 0.998. The limits of detection and the limits of quantification were ranged from 1.3×10−2 to 5.3×10−2 and 4.2×10−2 to 1.8×10−1 μg/L, respectively. At the end, the method was successfully applied to analyze real fruit juice samples and target analytes were not detected in real samples.

Agent-Based Model of Mass Campus Shooting: Comparing Hiding and Moving of Civilians

The follow-up impact of the campus shooting incident is enormous. Due to a large number of minors (vulnerable groups) and complex building environments, the school is one of the main targets of terrorists. The Sandy Hook Elementary School shooting in 2012 poses a challenge to social and public security. Considering the dichotomous characteristics of the people shot and the impact of the refuge area on the crowd movement in the shooting incident, we use an agent-based model to restore the real target case. Based on the optimal solution, we explore the effects of the number and location of building exits, the scale of the shooter's attack, and the time to find shelter on the shooting results. For building exits, we found that for the environment in the case, the exit location has little impact on the number of casualties, but the increase in the number of exits provides more evacuation routes for the crowd and reduces the number of casualties. Panic will increase the probability of collision and injury. Hiding in a refuge area away from the shooter is effective to avoid shooting injury. At the same time, we propose the critical point of hiding probability. When it is greater than 40%, the faster the escape speed is, the higher the safety is; escape is more likely to replace avoidance in hiding probability less than 40% of environments. These findings are helpful to guide the building evacuation channel construction and emergency evacuation training.

Improved criteria for evaluating impact targets in regulative drop tests of dangerous goods packagings

AbstractFor dangerous goods packagings, drop testing onto an essential unyielding target can be used to assess the mechanical resistance to impact loads. Adopted regulations like Agreement concerning the International Carriage of Dangerous Goods by Road (ADR)/Regulation concerning the International Carriage of Dangerous Goods by Rail (RID) require that the impact surface provided shall be integral with a mass at least 50 times than that of the heaviest package to be tested. The problem is that many manufacturers do not possess impact targets that satisfy the required 50 times mass ratio for regulative drop tests during series production. The objective of this work is to verify existing and define improved criteria for impact target structures based on systematic investigations. Previous evidence highlights the relevance of other parameters in addition to the mass ratio. Therefore, in this research, a variation of drop test parameters was carried out experimentally. Furthermore, numerical vibration analysis was applied to investigate the deformability of the impact surface. The results conclude that the mass ratio of 1:50 cannot be defined as a decisive criterion. In order to determine the influence of further drop test parameters, the research findings were used to validate a parametric model that assesses impact target deflection. An approximation quality of over 90% was achieved. As a result, new evaluation criteria are proposed. First, a method for identifying critical impact target designs is provided. Second, a new comprehensive formula compares the approximated maximum deflection of a real impact target to the respective theoretical threshold derived from a worst‐case assumption. In practice, this leads to great advantages in the evaluation of already installed impact targets for dangerous goods packagings.

Detection of Litchi Leaf Diseases and Insect Pests Based on Improved FCOS

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model’s generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases’ and pests’ prevention and control.

TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency

Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods.

Improved Real Time Target Tracking System Based on Cam-shift and Kalman Filtering Techniques

This paper presents an improved real time target tracking system based on Cam-Shift and advanced Kalman filter algorithms. The paper, proposes a simple low implementation cost scheme while keeping the high tracking performance. Two degrees of freedom target tracking system based on two hybrid stepper motors with their drive systems is fully designed and implemented to improve the tracking accuracy while keeping low-cost. The potential of the proposed algorithm lies in its ability to accurately detect, track and produce firing commands on the designated target while utilizing low cost on the shelf embedded Microcontroller boards. A low cost Universal Serial Port (USB) camera is used and integrated with the proposed algorithm in order to detect and track maneuvering targets. A series of experiments for stationary and moving objects are carried out for testing the proposed system efficiency. The proposed system enables accurate performance for tracking static and moving objects. Hence it provides a simple and low implementation cost scheme for real time object tracking applications.

A least square generative network based on invariant contrastive feature pair learning for multimodal MR image synthesis.

During MR-guided neurosurgical procedures, several factors may limit the acquisition of additional MR sequences, which are needed by neurosurgeons to adjust surgical plans or ensure complete tumor resection. Automatically synthesized MR contrasts generated from other available heterogeneous MR sequences could alleviate timing constraints. We propose a new multimodal MR synthesis approach leveraging a combination of MR modalities presenting glioblastomas to generate an additional modality. The proposed learning approach relies on a least square GAN (LSGAN) using an unsupervised contrastive learning strategy. We incorporate a contrastive encoder, which extracts an invariant contrastive representation from augmented pairs of the generated and real target MR contrasts. This contrastive representation describes a pair of features for each input channel, allowing to regularize the generator to be invariant to the high-frequency orientations. Moreover, when training the generator, we impose on the LSGAN loss another term reformulated as the combination of a reconstruction and a novel perception loss based on a pair of features. When compared to other multimodal MR synthesis approaches evaluated on the BraTS'18 brain dataset, the model yields the highest Dice score with [Formula: see text] and achieves the lowest variability information of [Formula: see text], with a probability rand index score of [Formula: see text] and a global consistency error of [Formula: see text]. The proposed model allows to generate reliable MR contrasts with enhanced tumors on the synthesized image using a brain tumor dataset (BraTS'18). In future work, we will perform a clinical evaluation of residual tumor segmentations during MR-guided neurosurgeries, where limited MR contrasts will be acquired during the procedure.

Modals model models: scientific modeling and counterfactual reasoning

Counterfactual reasoning has been used to account for many aspects of scientific reasoning. More recently, it has also been used to account for the scientific practice of modeling. Truth in a model is truth in a situation considered as counterfactual. When we reason with models, we reason with counterfactuals. Focusing on selected models like Bohr’s atom model or models of population dynamics, I present an account of how the imaginative development of a counterfactual supposition leads us from reality to interesting model assumptions; how it guides our reasoning from these assumptions to interesting consequences for the model scenario via counterfactual entailment; and how it leads us back to conclusions on real target phenomena.

Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Within the last decades, a large number of techniques for contrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks, such as object recognition. In this work, the effect of contrast enhancement algorithms is evaluated by means of the triangle orientation discrimination (TOD), which is a current method for imager performance assessment. The conventional TOD approach requires observers to recognize equilateral triangles pointing in four different directions, whereas here convolutional neural network models are used for the classification task. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed over natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then, these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms, such as the contrast-limited adaptive histogram equalization or local range modification, are applied. Then accuracies of the trained models on these images are compared for different contrast enhancement algorithms. Accuracy gains for low signal-to-background ratios and sufficiently large triangles are found, whereas impairments are found for high signal-to-background ratios and small triangles. A high generalization ability of our TOD model is found from the similar accuracies for several image databases used for backgrounds. Finally, implications of replacing triangles with real target signatures when using such advanced digital signal processing algorithms are discussed. The results are a step toward the assessment of those algorithms for generic target recognition.

A real time target face tracking algorithm based on saliency detection and Camshift

A real time target face tracking algorithm based on saliency detection and Camshift

Effective path-loss compensation model based on multipath exploitation for through-the-wall radar imaging

Through-the-wall radar imaging (TWRI) has attracted a great deal of attention in several sensitive applications, including rescue missions and military operations. Notwithstanding its broad range of applications, TWRI suffers from path-loss because distant targets experience more attenuation of signal power than those closer to the transceiver. This challenge may lead to missed targets with important information necessary for analysis and informed decision making. Responding to the challenge, we have developed a signal model with an effective path-loss compensator incorporating a free space exponent. Furthermore, multipath exploitation and compressive sensing techniques were employed to develop an effective algorithm for isolating residual clutter that may corrupt real targets. The proposed signal model integrates contributions from the front wall, multipath returns, and path-loss. Compared with the state-of-the-art model under the same experimental conditions, simulation results show that the proposed model achieves improved signal-to-clutter ratio, relative clutter peak, and probability of detection by 13.1%, 17.4% and 33.6%, respectively, suggesting that our model can represent the scene more accurately.

Closed‐form angle estimation of unresolved targets in monopulse radar to counter the angular electronic attack

AbstractTypical monopulse radar cannot detect the angle of two closely‐spaced targets in one resolution cell. The authors propose a closed‐form technique to estimate the angle of two unresolved targets by using a single snapshot of four radar receiver channels. In this work, phase comparison and amplitude comparison problems for monopulse radar are resolved. The monopulse radar angle estimation system has been enhanced using the diagonal difference channel. Using a single pulse, this technique can accurately determine the angle and amplitude of the deceptive decoy and the real target. If both targets have the same direction, the proposed scheme cannot detect them; therefore, to estimate the angle of such targets, we used an additional antenna. The effectiveness of the proposed method is validated by comparing the exact solutions and simulations.

Dim and Small Target Detection Based on Spatio-Temporal Filtering and High-Order Energy Estimation

To improve the detection ability of infrared dim and small targets in complex cloud backgrounds, we present a novel method based on motion energy estimation. First, according to the characteristic of the gradient between the target and the background, we propose a new diffusion function and combine with the gradient difference to complete the background suppression. Then an improved high-order correlation algorithm is proposed to enhance the target signal through the motion correlation properties of the target, which build the gradient difference discrimination model between the target and four adjacent directions to establish the region of interest model, then realizes the high-order energy enhancement of the region of interest and further eliminate the clutters at the same time. Finally, the motion model of the target in the inter-frame spatial-temporal domain is constructed to estimate the motion direction of the target, then realizes the target energy accumulation along the motion direction and obtains the maximum energy of the target to detect the real target. In the experiment, we select six scenes for testing, the experimental results show that the proposed method has an excellent detection performance compared with six traditional detection algorithms in infrared dim and small target detection.

Introduction to Surgical Navigation in Oral Surgery: A Case-Series

The application of surgical navigation in oral and maxillo-facial surgery has been increasing over time. In fact, computer-assisted surgery provides real-time, precise, and accurate position and guidance during surgery. The purpose of our work is to introduce the evolution of surgical navigation in recent decades, describe some technical aspects of this technology, explore new possibilities of application of surgical navigation in oral surgery, and validate the accuracy of computer-assisted surgery. We included four patients in our sample who underwent virtual planning on the cone beam CT data set and surgical navigation using non-invasive fiducial markers. The first patient presented a dislocated orthodontic arch in the soft tissues of the cheek, while the other patients presented supernumerary and impacted dental elements. Among them, two patients were affected by craniofacial synostosis. We evaluated the accuracy of computer-assisted surgery, calculating the discrepancy between the real and virtual target. In all cases, the target registration error was less than or equal to 1 mm. We can affirm that surgical navigation is a valid tool to enhance oral surgery, guaranteeing an undoubted advantage in terms of the reliability and predictability of the results, especially in complex cases.

A Nonparametric Regression-Based Multi-Scale Gradient Correlation Filtering Method for Infrared Small Target Detection

Infrared small target detection, especially under low SCR conditions and complex backgrounds, is still a challenging research task. Considering the scale change caused by small targets rapidly moving, in this paper, a nonparametric regression-based multi-scale gradient correlation filtering (MGCF) detection method is proposed. First, a nonparametric regression method is applied to calculate the gradient of each point. Then, based on the unique gradient characteristics of small targets, a multi-scale gradient correlation (MGC) template is designed to distinguish small targets from clutter. After that, a multi-scale gradient correlation filtering method is proposed to enhance the target intensity and suppress clutter. At last, based on the obtained filtering response, an adaptive threshold segmentation method is adopted to extract real small targets. Experimental results demonstrate that the proposed method can fully improve the signal-to-clutter ratio (SCR) of small targets under different complex backgrounds. Moreover, compared with other baseline methods, the proposed method exhibits excellent detection performance.

Analysis of Intentional Electromagnetic Interference on GENEC Model Using Cylindrical Mode Matching

In recent times, due to the high operating frequency and low operating voltage of modern electronic devices, intended electromagnetic interference (IEMI) has been the cause of increasing damage. In particular, targets with precision electronics such as aircrafts or missiles have shown that a high-power microwave (HPM) may cause malfunction or partial destruction of the GPS or the avionic control system. Analysis of the effects of IEMI requires electromagnetic numerical analyses. However, there are limitations to conventional numerical techniques, such as the finite element method, method of moment, or finite difference time domain method, due to the complexity and large electrical length of a real target system. In this paper, we proposed a new cylindrical mode matching (CMM) technique to analyze IEMI of the generic missile (GENEC) model, which is a hollow metal cylinder with multiple apertures. Using the CMM, we can quickly analyze the effect of the IEMI inside the GENEC model from 1.7 to 2.5 GHz. The results were compared with those of the measurements and, for verification, with the FEKO, a commercial software program developed by Altair Engineering, and showed good agreement. In this paper, the electro-optic (EO) probe was used to measure the electric field inside the GENEC model.

Local Convergence Index-Based Infrared Small Target Detection against Complex Scenes

Infrared small target detection (ISTD) plays a crucial role in precision guidance, anti-missile interception, and military early-warning systems. Existing approaches suffer from high false alarm rates and low detection rates when detecting dim and small targets in complex scenes. A robust scheme for automatically detecting infrared small targets is proposed to address this problem. First, a gradient weighting technique with high sensitivity was used for extracting target candidates. Second, a new collection of features based on local convergence index (LCI) filters with a strong representation of dim or arbitrarily shaped targets was extracted for each candidate. Finally, the collective set of features was inputted to a random undersampling boosting classifier (RUSBoost) to discriminate the real targets from false-alarm candidates. Extensive experiments on public datasets NUDT-SIRST and NUAA-SIRST showed that the proposed method achieved competitive performance with state-of-the-art (SOTA) algorithms. It is also important to note that the average processing time was as low as 0.07 s per frame with low time consumption, which is beneficial for practical applications.

A longan yield estimation approach based on UAV images and deep learning.

Longan yield estimation is an important practice before longan harvests. Statistical longan yield data can provide an important reference for market pricing and improving harvest efficiency and can directly determine the economic benefits of longan orchards. At present, the statistical work concerning longan yields requires high labor costs. Aiming at the task of longan yield estimation, combined with deep learning and regression analysis technology, this study proposed a method to calculate longan yield in complex natural environment. First, a UAV was used to collect video images of a longan canopy at the mature stage. Second, the CF-YD model and SF-YD model were constructed to identify Cluster_Fruits and Single_Fruits, respectively, realizing the task of automatically identifying the number of targets directly from images. Finally, according to the sample data collected from real orchards, a regression analysis was carried out on the target quantity detected by the model and the real target quantity, and estimation models were constructed for determining the Cluster_Fruits on a single longan tree and the Single_Fruits on a single Cluster_Fruit. Then, an error analysis was conducted on the data obtained from the manual counting process and the estimation model, and the average error rate regarding the number of Cluster_Fruits was 2.66%, while the average error rate regarding the number of Single_Fruits was 2.99%. The results show that the method proposed in this paper is effective at estimating longan yields and can provide guidance for improving the efficiency of longan fruit harvests.

Research on Interrupted Sampling Repeater Jamming Performance Based on Joint Frequency Shift/Phase Modulation.

Interrupted sampling repeater jamming (ISRJ) is a classic active coherent jamming. Due to its structural limitations, it has inherent defects such as a discontinuous time-frequency (TF) distribution, strong distribution regularity of pulse compression results, limited jamming amplitude, and strong false targets lagging behind the real target. These defects have not been fully resolved yet due to the limitation of the theoretical analysis system. Based on the analysis of the influence factors of ISRJ on the interference performance for linear-frequency-modulated (LFM) and phase-coded signals, this paper proposes an improved ISRJ method based on the joint subsection frequency shift and two-phase modulation. The coherent superposition of jamming signals at different positions for LFM signals is achieved by controlling the frequency shift matrix and the phase modulation parameters to form a strong pre-lead false target or multiple positions and ranges of blanket jamming areas. For the phase-coded signal, the pre-lead false targets are generated through code prediction and the two-phase modulation of the code sequence, resulting in similar noise interference. The simulation results show that this method can overcome the inherent defects of ISRJ.

Agent-based modeling of mass shooting case with the counterforce of policemen

AbstractMass shooting cases have caused large casualties worldwide. The counterforce, such as the policemen, is of great significance to reducing casualties, which is the core issue of social safety governance. Therefore, we model both the killing force and counterforce, to explore the crowd dynamics under the shooting. Taking the “Borderline” shooting in 2018 as the target case, the agent-based modeling is applied to back-calculate this dynamic process and explore key behavior rules of individuals. The real death tolls of three classes of agents (civilians, policemen, & killers) are as the real function, based on which we calculate the gaps between real target case and simulations. Eventually, we obtain three optimal solutions, which achieve the least gap or highest matching degree. Besides, we make counterfactual inferences under the optimal solutions, to explore the strategic interactions between policemen and killers. For strategies of killers, we explore different sizes, positions, and moving patterns of the killers. The optimal size of policemen is four to five, for each one killer. For strategies of policemen, we explore the size, locations, and the response time. It indicates that optimal response time of policemen is thirty to forty shots of the killer, and the death of civilians and policemen can be minimized, and the death probability of the killer can be maximized. These findings help to improve public safety governance for our cities. To effectively deal with sudden shooting terrorist cases, patrol routes, reasonable settings, and swift dispatches of the police (stations) should be considered.