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In order to evaluate the tracking capability of optoelectric tracking for an orbital target, the limit magnitude detection performance calculation model and its calculation method are studied. Combining the optical signal characteristics of the tracked orbital target, the background, and the CCD noise, the framework of the limit magnitude calculation model of the system for dynamic target detection is constructed. The relationships between the limit magnitude and the signal-to-noise ratio threshold of the optical signal characteristics, the exposure time of the CCD camera, and the dark current of the CCD imaging are studied and analyzed while considering the sunlight illumination condition, so that the calculation function and its change curve are given. The limit magnitude detection capability of the system is verified by the simulated experiment and the synchronized tracking test, and the detection distance maximum error of the model calculation is 3.6 m. The results show that under certain illumination conditions, when the exposure time of the CCD camera is longer and the SNR threshold is lower, the limit magnitude detection performance of the system is better, and the tracking performance of the system is more stable. [ABSTRACT FROM AUTHOR]

Utilizing spatial diversity, the distributed multiple-input multiple-output (MIMO) radar has the potential advantage of improving system detection performance. In this paper, the incoherent detection performance of distributed multiple-input multiple-output (MIMO) radars is investigated for Rice fluctuating targets. To calculate the incoherent detection probability, the moment generating function (MGF) of the Rice variable is expanded as the infinite series form. By inverting the product of MGFs of multiple independent Rice variables, new closed-form expressions for the probability density function (PDF) of the sum of independent and weighted squares of Rice variables are proposed. The proposed PDF expression for the sum of independent, non-identically distributed (i.n.i.d.) Rice variables involves an infinite series in terms of the confluent Lauricella function. Specially, the PDF for the sum of independent identically distributed (i.i.d.) Rice is expressed as the confluent hypergeometric function-based infinite series. In addition, the uniform convergence of the proposed PDF expression is also validated. Using this proposed expression, the closed-form and approximate expressions of the incoherent detection probability of MIMO radar are derived, respectively. Numerically evaluated results are illustrated and compared with Monte Carlo (MC) simulations to validate the accuracy of the derivations. [ABSTRACT FROM AUTHOR]

Objective: This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems. Background: Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven. Method: Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods. Results: Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found. Conclusion: Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo. Application: The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces. [ABSTRACT FROM AUTHOR]

Based on observational data from the ADTD 2D and VLF/LF 3D lightning location systems in Yunnan Province from 2017 to 2020, this study compared the differences in the distribution of cloud-toground (CG) lightning parameters between the two systems. In situations where obtaining information on the actual location and current intensity of lightning is challenging, this study researched the identification and matching method and spatiotemporal threshold selection method for the same CG lightning stroke event by using inversion data from both systems. The detection efficiency of both systems was also analyzed. The results show significant differences in the spatial distribution of CG lightning stroke density and lightning current amplitude between the two systems in eastern, southwestern, and northwestern Yunnan. While the 3D system showed a higher lightning stroke density, the 2D system recorded a higher average lightning current amplitude. Both systems'positioning data revealed a higher proportion of singlestroke CG lightning, with a higher proportion of positive CG lightning compared with negative CG lightning. The detection efficiency of the 3D system for small amplitude currents (0-5 kA) with negative lightning strokes has been improved. By using a spatiotemporal threshold matching window of 1 ms and 10 km, we found that the positioning distance deviation of the two systems for the same stroke event was mainly concentrated within 1 km, and the time difference was within 0.05 ms. The temporal and spatial deviation of matched return strokes was relatively small in central and southwestern Yunnan but larger in northwestern Yunnan. Considering parameters such as lightning stroke polarity, correlation coefficient, relative error, amplitude ratio, and cumulative probability of amplitude, we found that the two systems had good consistency in the inversion results of current intensity for the same return stroke, indicating that the identification and matching method for the same lightning stroke event was objective and reliable. [ABSTRACT FROM AUTHOR]

Liquid scintillator detectors have great advantages in the field of radionuclide detection because of their low detection limit, high sensitivity, and diverse functions. However, the material properties of liquid scintillators directly determine their detection effectiveness, which leads to their poor vertex resolution and particle identification. In this work, we introduce the improvement methods, choices, and properties of different novel liquid scintillator materials in recent years. This article is expected to provide references for the development and research of liquid scintillator materials in various application fields. [ABSTRACT FROM AUTHOR]

Objectives: Respiratory infections are among the most common infectious diseases, resulting in significant morbidity and mortality. C-reactive protein (CRP), serum amyloid A (SAA), procalcitonin (PCT), and interleukin-6 (IL-6) are advantageous for diagnosing respiratory tract infections. This study assessed the analytical performance and accuracy of new kits for Eu3+-based CRP/SAA and PCT/IL-6 lateral flow immunoassay and its diagnostic value in respiratory tract infections. Methods: This study evaluated the detection performance of a test kit using guidelines from the Center for Medical Device Evaluation (CMDE) and the Clinical and Laboratory Standards Institute (CLSI). The test results were compared to those of the commercial kits (CRP: Mindray; SAA: Norman; PCT: Shanghai Upper; IL-6: Wantai BioPharm). A total of 156 patients with respiratory tract infections (53 with bacterial infections (Bac group); 50 with viral infections (Vir group); and 53 with co-infections (Bac + Vir group)) were enrolled, along with 50 healthy controls (HC group). Venous blood samples were collected to measure levels of SAA, PCT, CRP, and IL-6 using both the test and commercial kits. The diagnostic value of these biomarkers was assessed using receiver operating characteristic (ROC) curves. Results: Correlation analysis demonstrated a strong concordance between the test kits and commercial kits (CRP: r = 0.9396, P

Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SINR) between the target and the echo is proposed. In view of the challenges brought by the traditional water-filling algorithm, this paper further studies how to effectively solve the new optimization criteria to improve the overall performance of the system. Specifically, this paper proposes a PCMA-ES algorithm that combines an adaptive penalty function with the Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) algorithm. The penalty function aims to prioritize feasible solutions by assigning them the highest fitness. For infeasible solutions with lower constraint violations, the fitness is slightly lower, allowing for better utilization of information from infeasible solutions. The simulation results show that the PCMA-ES algorithm has lower time complexity or better performance than the traditional water-filling algorithm, and can solve more complex transmission waveforms. In addition, the waveform designed with a joint optimization criterion outperforms that based on a single optimization criterion. The radar detection focus can be adjusted to meet the specific requirements of diverse detection tasks.

In order to evaluate the tracking capability of optoelectric tracking for an orbital target, the limit magnitude detection performance calculation model and its calculation method are studied. Combining the optical signal characteristics of the tracked orbital target, the background, and the CCD noise, the framework of the limit magnitude calculation model of the system for dynamic target detection is constructed. The relationships between the limit magnitude and the signal-to-noise ratio threshold of the optical signal characteristics, the exposure time of the CCD camera, and the dark current of the CCD imaging are studied and analyzed while considering the sunlight illumination condition, so that the calculation function and its change curve are given. The limit magnitude detection capability of the system is verified by the simulated experiment and the synchronized tracking test, and the detection distance maximum error of the model calculation is 3.6 m. The results show that under certain illumination conditions, when the exposure time of the CCD camera is longer and the SNR threshold is lower, the limit magnitude detection performance of the system is better, and the tracking performance of the system is more stable.

Utilizing spatial diversity, the distributed multiple-input multiple-output (MIMO) radar has the potential advantage of improving system detection performance. In this paper, the incoherent detection performance of distributed multiple-input multiple-output (MIMO) radars is investigated for Rice fluctuating targets. To calculate the incoherent detection probability, the moment generating function (MGF) of the Rice variable is expanded as the infinite series form. By inverting the product of MGFs of multiple independent Rice variables, new closed-form expressions for the probability density function (PDF) of the sum of independent and weighted squares of Rice variables are proposed. The proposed PDF expression for the sum of independent, non-identically distributed (i.n.i.d.) Rice variables involves an infinite series in terms of the confluent Lauricella function. Specially, the PDF for the sum of independent identically distributed (i.i.d.) Rice is expressed as the confluent hypergeometric function-based infinite series. In addition, the uniform convergence of the proposed PDF expression is also validated. Using this proposed expression, the closed-form and approximate expressions of the incoherent detection probability of MIMO radar are derived, respectively. Numerically evaluated results are illustrated and compared with Monte Carlo (MC) simulations to validate the accuracy of the derivations.

A quicker and easier decision-making detection algorithm is important for mobile-phone application to support strawberry management. However, lightweight is a big challenge, including accuracy, speed, and complex conditions confronted by the fruit detection algorithm. For this reason, a lightweight YOLOStrawberry was proposed in this paper based on modified YOLOv5 architecture and compared to other YOLO-lightweight algorithms. YOLOStrawberry incorporated Conv_Maxpool, Shuffle_Block, ResNet, SElayer and SPPF as Backbone network, FPN as Neck network, and CIoU loss function to improve the detection performance of strawberry fruit. The obtained weight-size of YOLOStrawberry is 3.37 MB, which is in third position compared to 1.57 MB for v5lite-e, 3.19 MB for v5lite-s, 3.56 MB for YOLOv5n, 8.60 MB for v5lite-c and 10.7 MB for v5lite-g. Nevertheless, the required trained time for YOLOStrawberry is 0.93%, 0.69%, 0.62%, 0.71%, 1.14% less than v5lite-c, v5lite-e, v5lite-g, v5lite-s and YOLOv5n, respectively. Meanwhile, the average precision (AP) detection level is measured as 89.7% of YOLOStrawberry is 0.4%, 0.7%, 3%, 3.4% and 9.1% more accurate than v5lite-g, YOLOv5n, v5lite-c, v5lite-s and v5lite-e, respectively. Furthermore, the tested speed time of YOLOStrawberry is 7.30 ms, faster compared to 11.7 ms for YOLOv5n, 13.2 ms for v5lite-g, 13.3 ms for v5lite-e, 14.0 ms for v5lite-s, and 16.2 ms for v5lite-c. Therefore, YOLOStrawberry algorithm is lightweight, robust, accurate, fast, applicable to mobile-phone for real-time and extendable to other fruits for detection. [ABSTRACT FROM AUTHOR]

A coil-type resistivity logging tool has been proposed for multi-frequency operation (250 kHz to 8 MHz) based on electromagnetic wave propagation. Different frequencies are matched with specific transmission coils, while the same two reception coils are used to achieve a consistent depth of investigation (DOI). By analyzing the electromagnetic attenuation and phase difference of induced signals at different frequencies, the complex resistivity can be jointly inverted. The coil systems were designed with four DOI options (0.3 m, 0.5 m, 1 m, and 1.5 m) and six measurement frequencies (250 kHz, 500 kHz, 1 MHz, 2 MHz, 4 MHz, and 8 MHz). Their detection performance was evaluated using the finite element method on the COMSOL platform. For higher frequencies or a deeper DOI, a coil system with a larger source-receiver distance was selected. These designed coil systems can provide qualitative identification of formations with thicknesses greater than 0.05 m and quantitative identification of formations with thicknesses greater than 1.5 m. In the single-transmission, dual-reception coil system, response signals are distorted at the formation boundary, and this distortion increases with the source-receiver distance. Adding a secondary transmission coil can reduce the distortion of response signals at the formation interface without increasing the overall length of the coil system. This research enriches the theoretical framework of complex resistivity spectrum (CRS) logging and contributes to the commercial development of CRS logging tools. [ABSTRACT FROM AUTHOR]

The recent outbreak of monkeypox has raised significant concerns in the field of public health, primarily because it has quickly spread to over 40 countries outside of Africa. Detecting monkeypox in its early stages can be quite challenging because its symptoms can resemble those of chickenpox and measles. However, there is hope that potential use of computer-assisted tools may be used to identify monkeypox cases rapidly and efficiently. A promising approach involves the use of technology, specifically deep learning methods, which have proven effective in automatically detecting skin lesions when sufficient training examples are available. To improve monkeypox diagnosis through mobile applications, we have employed a particular neural network called MobileNetV2, which falls under the category of Fully Connected Convolutional Neural Networks (FCCNN). It enables us to identify suspected monkeypox cases accurately compared to classical machine learning approaches. The proposed approach was evaluated using the recall, precision, F score, and accuracy. The experimental results show that our architecture achieves an accuracy of 0.99%, a Recall of 1.0%, an F-score of 0.98%, and a Precision of 0.95%. We believe that such experimental evaluation will contribute to the medical domain and many use cases. [ABSTRACT FROM AUTHOR]

Detection canines serve critical roles to support the military, homeland security and border protection. Some explosive detection tasks are physically demanding for dogs, and prior research suggests this can lead to a reduction in olfactory detection sensitivity. To further evaluate the effect of exercise intensity on olfactory sensitivity, we developed a novel olfactory paradigm that allowed us to measure olfactory detection thresholds while dogs exercised on a treadmill at two different exercise intensities. Dogs (n = 3) showed a decrement in olfactory detection for 1-bromooctane at 10−3 (v/v) dilutions and lower under greater exercise intensity. Dogs' hit rate for the lowest concentration dropped from 0.87 ± 0.04 when walking at low intensity to below 0.45 ± 0.06 when trotting at moderate intensity. This decline had an interaction with the duration of the session in moderate intensity exercise, whereby dogs performed near 100% detection in the first 10 min of the 8 km/h session, but showed 0% detection after 20 min. Hit rates for high odor concentrations (10−2) were relatively stable at both low (1 ± 0.00) and moderate (0.91 ± 0.04) exercise intensities. The paradigm and apparatus developed here may be useful to help further understand causes of operationally relevant olfactory detection threshold decline in dogs.

Objectives: Preeclampsia is a common pregnancy complication that significantly contributes to maternal mortality, perinatal mortality, and preterm delivery. The sFlt-1/PlGF (fms-like tyrosine kinase-1/placental growth factor) ratio has demonstrated robust diagnostic value for preeclampsia. This study assessed the analytical performance and diagnostic accuracy of a novel quantitative determination kit for sFlt-1 and PlGF for the diagnosis of preeclampsia. Methods: The detection performance of the test kit was validated using the Center for Medical Device Evaluation (CMDE) and Clinical and Laboratory Standards Institute (CLSI) documents. The test results were compared to those of the Elecsys immunoassay (Roche Diagnostics). Independent discovery and validation sets were used to analyze the diagnostic efficacy of the preeclampsia kit. The area under the curve (AUC) for preeclampsia at different gestational ages was calculated. Results: Correlation analysis between the test and Roche kits revealed a strong concordance (sFlt-1: r = 0.9966, P

To address the issues of low efficiency and high complexity of detection models for electric power workers in distribution rooms, the electric power worker identification approach is proposed. The ArcFace loss function is used as the coordinate regression loss of the target box. According to the score, the template box with the highest score is selected for prediction, which speeds up the rate of convergence. Dimensional clustering is used to set template boxes for bounding box prediction. The experimental results show that the improved YOLOv3 is a high-performance and lightweight model. The electric power worker identification approach proposed in this paper has a high-speed recognition process, accurate recognition results. The effectiveness of the approach is verified with better detection performance and robustness.

Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts, by performing binary classification. While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist. In this paper, we propose a general linguistic steganalysis framework named LS-MTL, which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts. LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed model. In the proposed framework, convolutional neural networks (CNNs) are utilized as private base models to extract sensitive features for each steganalysis task. Besides, a shared CNN is built to capture potential interaction information and share linguistic features among all tasks. Finally, LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or non-steganographic. Experimental results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task, while average Acc, Pre, and Rec are increased by 0.5%, 1.4%, and 0.4%, respectively. More ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data. [ABSTRACT FROM AUTHOR]

To detect knee disease, radiologists have been utilizing multi-view images such as computer tomography (CT) scans, MRIs, and X-rays. The cheapest method is X-ray to attain the images that is used widely. There exist various image processing techniques to detect knee disease at the initial stages; however, there is still room for improved accuracy and precision of the existing algorithms. Furthermore, in machine learning-based techniques, hand-crafted feature extraction mechanism is a tedious task. Therefore, in this paper, we suggest a technique based on customized CenterNet with a pixel-wise voting scheme to extract the features automatically. Our model uses the most representative features due to the best localization results and a weighted pixel-wise voting scheme which takes input from a predicted bounding box using modified CenterNet. It gives a more accurate bounding box based on the voting score from each pixel inside the former box. Moreover, we employed the distillation knowledge concept to make our model simple without increasing its computational cost, and transfer knowledge from a complex network to a simple network. Therefore, our proposed model detects the KOA in knee images precisely and determines its severity level according to the KL grading system such as Grade-I, Grade-II, Grade-III, and Grade-IV. Our proposed model is a robust and improved architecture based on CenterNet utilizing a simple DenseNet-201 as a base network for feature extraction. Due to the dense blocks employed in a base network, most representative features are extracted from the knee samples. We employed two benchmarks i.e. Mendeley VI for the training, and testing, and the OAI dataset for cross-validation. We evaluated the performance of the proposed technique using various experiments and it is estimated that our proposed model outperforms the existing techniques with an accuracy of 99.14% over testing and 98.97% over cross-validation.

The 5G network is promised to provide all the requirements surfacing from the speedily rising number of mobile phone users rather than huge amount of data transmission. Many 5G waveforms can be used by 5G network such as Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM), Universal Filtered Multi-Carrier (UFMC), or Filter Bank Multi-Carrier (FBMC) waveform. To effectively capitalize on the spectrum resources, Internet of Thing (IoT), and multimedia transmission, the Cooperative Spectrum Sensing (CSS) system is being extensively utilized by Cognitive Radio (CR). It is able to meet the requisite communication services, however, the conventional CSS systems have been designed to detect only single kind of 5G waveform and poor detection performance for low signal-to-noise ratio (SNR). In this paper, a hybrid filter based CSS system is proposed for detection of various 5G waveforms by multi-user. The proposed filter includes three cascaded stages: cosine filtering, Welch segmentation, and Hamming windowing. The proposed detection system is applied on different data such as various of waveforms and SNRs. The simulation results exhibit a significant detection performance for the parameters of less than zero dB of SNR, greater than 95% global detection probability, less than 1% global system error probability, and less than 1% global false alarm probability. In addition, the detection performance reveals that the proposed system is outperformed related works as shown in the comparison in terms of the previous parameters.

To overcome the influence of the daytime skylight background on long-distance optical detection, a new type of shearing interference detection system was proposed to improve the detection performance of the traditional detection system for finding dark objects such as dim stars during the daytime. This article focuses on the basic principle and mathematical model as well as the simulation and experimental research of the new type of shearing interference detection system. The comparison of the detection performance between this new-type detection system and the traditional system is also carried out in this article. The experimental results show that the detection performance of the new type of shearing interference detection system is significantly better than that of the traditional system, and the image signal-to-noise ratio of this new-type system (about 13.2) is much higher than that of the best result of the traditional detection system (about 5.1). [ABSTRACT FROM AUTHOR]

Backscatter communication (BC) is a promising technology for low-power and low-data-rate applications, though the signal detection performance is limited since the backscattered signal is usually much weaker than the original signal. When the detection performance is poor, the backscatter device (BD) may not be able to accurately detect and interpret the incoming signal, leading to errors and degraded communication quality. This can result in data loss, slow data transfer rates, and reduced reliability of the communication link. This paper proposes a novel approach to improve the detection performance of backscatter communication systems using evolutionary deep learning. In particular, we focus on training deep convolutional neural networks (DCNNs) to improve the detection performance of BC. We first develop a novel hybrid algorithm based on artificial bee colony (ABC), biogeography-based optimization (BBO), and particle swarm optimization (PSO) to optimize the architecture of the DCNN, followed by training using a large set of benchmark datasets. To develop the hybrid ABC, the migration operator of the BBO is used to improve the exploitation. Moving towards the global best of PSO is also proposed to improve the exploration of the ABC. Then, we take advantage of the proposed deep architecture to improve the bit-error rate (BER) performance of the studied BC system. The simulation results demonstrate that the proposed algorithm has the best performance in training the benchmark datasets. The results also show that the proposed approach significantly improves the detection performance of backscattered signals compared to existing works. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) is widely used in autonomous vehicles to obtain precise 3D information about surrounding road environments. However, under bad weather conditions, such as rain, snow, and fog, LiDAR-detection performance is reduced. This effect has hardly been verified in actual road environments. In this study, tests were conducted with different precipitation levels (10, 20, 30, and 40 mm/h) and fog visibilities (50, 100, and 150 m) on actual roads. Square test objects (60 × 60 cm2) made of retroreflective film, aluminum, steel, black sheet, and plastic, commonly used in Korean road traffic signs, were investigated. Number of point clouds (NPC) and intensity (reflection value of points) were selected as LiDAR performance indicators. These indicators decreased with deteriorating weather in order of light rain (10–20 mm/h), weak fog (<150 m), intense rain (30–40 mm/h), and thick fog (≤50 m). Retroreflective film preserved at least 74% of the NPC under clear conditions with intense rain (30–40 mm/h) and thick fog (<50 m). Aluminum and steel showed non-observation for distances of 20–30 m under these conditions. ANOVA and post hoc tests suggested that these performance reductions were statistically significant. Such empirical tests should clarify the LiDAR performance degradation. [ABSTRACT FROM AUTHOR]

A coil-type resistivity logging tool has been proposed for multi-frequency operation (250 kHz to 8 MHz) based on electromagnetic wave propagation. Different frequencies are matched with specific transmission coils, while the same two reception coils are used to achieve a consistent depth of investigation (DOI). By analyzing the electromagnetic attenuation and phase difference of induced signals at different frequencies, the complex resistivity can be jointly inverted. The coil systems were designed with four DOI options (0.3 m, 0.5 m, 1 m, and 1.5 m) and six measurement frequencies (250 kHz, 500 kHz, 1 MHz, 2 MHz, 4 MHz, and 8 MHz). Their detection performance was evaluated using the finite element method on the COMSOL platform. For higher frequencies or a deeper DOI, a coil system with a larger source-receiver distance was selected. These designed coil systems can provide qualitative identification of formations with thicknesses greater than 0.05 m and quantitative identification of formations with thicknesses greater than 1.5 m. In the single-transmission, dual-reception coil system, response signals are distorted at the formation boundary, and this distortion increases with the source-receiver distance. Adding a secondary transmission coil can reduce the distortion of response signals at the formation interface without increasing the overall length of the coil system. This research enriches the theoretical framework of complex resistivity spectrum (CRS) logging and contributes to the commercial development of CRS logging tools.

The recent outbreak of monkeypox has raised significant concerns in the field of public health, primarily because it has quickly spread to over 40 countries outside of Africa. Detecting monkeypox in its early stages can be quite challenging because its symptoms can resemble those of chickenpox and measles. However, there is hope that potential use of computer-assisted tools may be used to identify monkeypox cases rapidly and efficiently. A promising approach involves the use of technology, specifically deep learning methods, which have proven effective in automatically detecting skin lesions when sufficient training examples are available. To improve monkeypox diagnosis through mobile applications, we have employed a particular neural network called MobileNetV2, which falls under the category of Fully Connected Convolutional Neural Networks (FCCNN). It enables us to identify suspected monkeypox cases accurately compared to classical machine learning approaches. The proposed approach was evaluated using the recall, precision, F score, and accuracy. The experimental results show that our architecture achieves an accuracy of 0.99%, a Recall of 1.0%, an F-score of 0.98%, and a Precision of 0.95%. We believe that such experimental evaluation will contribute to the medical domain and many use cases.

Caves in temperate regions are characterized by food scarcity compared to surface habitats. Therefore, hypotheses on adaptation to cave life suggest that many characteristics, such as resistance to starvation, reduced energy demand, and increased food-finding ability, have evolved among cave dwellers to cope with food frugality. To test the hypothesis involving increased food-finding ability, the prey detection performances of three surface and three subterranean populations of the Pyrenean newt, Calotriton asper, were compared. First, the rapidity of surface individuals in capturing prey at short distances inside a restricted aquarium under dark and light conditions and their score for capturing prey in a larger aquarium under similar conditions were measured. Simultaneously the scores obtained from individuals in cave populations in darkness were compared. Individuals from one of the surface population were maintained in darkness for approximately five years and retested. The surface C. asper individuals captured prey faster at short distances under light conditions than in total darkness; however, the differences were not significant. The scores of the surface C. asper individuals in the large aquarium were significantly better in darkness than in light conditions. In addition, the scores of cave populations at short distances in darkness were better than those of epigean populations in light conditions; however, the differences were not significant. Finally, the scores of surface individuals maintained in darkness for five years improved. To explain these results, it should be noted that surface C. asper populations forage at night using non-visual cues (e.g., chemical and mechanical cues), further suggesting that vision may somewhat inhibit other senses. This ability to forage at night may be favorable for cave colonization. Moreover, some hypogean populations, when the use of prey-detecting non-visual senses has developed, may start to exhibit adaptivity.

As a promising detector for room temperature nuclear radiation detection, all-inorganic perovskite CsPbBr3 has attracted great attention. However, the CsPbBr3 α-particle detector lacks detector architecture design and fabrication process optimization, leading to a low detection performance and detection stability. In this paper, three typical metal–semiconductor–metal-nanostructured CsPbBr3 α-particle detectors were designed and fabricated. The interfacially designed detector had an excellent energy resolution of 5.70% and long-term detection stability for more than 8 h. The strategy is simple and reproducible, but it provides a good idea for simultaneously improving the detection performance and detection stability of the CsPbBr3 α-particle detector. [ABSTRACT FROM AUTHOR]

• The aim was to investigate the effect of aging on the detection, under time constraints, of vulnerable road users. • The detection of vulnerable road users decreased with aging, even in the younger-old group. • Older-old adults compensated for their visual-field decline by doing more visual exploration than the other two groups did. • Further studies should investigate the detection of vulnerable road users, including those on personal mobility devices. The detection of vulnerable road users (VRUs), especially under time constraints, may be impaired in elderly drivers, due to their visual and cognitive decline. This represents a major concern for road safety. The objective of the present study was to investigate the effect of aging on the detection of VRUs. A further aim was to investigate the impact of external factors on VRU detection. Twenty-two young adults, 20 younger-old adults, and 32 older-old adults were included in the study. A series of photographs were displayed for 500 ms. Participants were asked to detect the VRU, which could be a motorcyclist, a cyclist, or a pedestrian. The VRU was located at one of two distances (near/far), one of two locations (off-centered/centered), and in two car-DRL (daytime running lights) environments (on/off). The ability to correctly detect a VRU was measured. An eye-tracker was used to record eye movements. The main findings showed that VRU-detection performance decreased with aging, even in the younger-old group (ages 55–68). The ability to correctly detect a VRU by older-old adults was poorer particularly when the VRU was a motorcyclist or a cyclist. As a whole, the older-old adults made more fixations to correctly detect the VRU than the other two age groups did. Moreover, the visual angle between the gaze of the participant's last fixation and the target in the older-old group was lower than in the other two groups, particularly when the VRU was off-centered. This finding suggests that older-old adults compensated for their visual-field decline by doing more visual exploration than the other two groups did to correctly detect the VRU. The results are discussed with regards to age-related cognitive and visual deficits. Word count: 279. [ABSTRACT FROM AUTHOR]

The 5G network is promised to provide all the requirements surfacing from the speedily rising number of mobile phone users rather than huge amount of data transmission. Many 5G waveforms can be used by 5G network such as Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM), Universal Filtered Multi-Carrier (UFMC), or Filter Bank Multi-Carrier (FBMC) waveform. To effectively capitalize on the spectrum resources, Internet of Thing (IoT), and multimedia transmission, the Cooperative Spectrum Sensing (CSS) system is being extensively utilized by Cognitive Radio (CR). It is able to meet the requisite communication services, however, the conventional CSS systems have been designed to detect only single kind of 5G waveform and poor detection performance for low signal-to-noise ratio (SNR). In this paper, a hybrid filter based CSS system is proposed for detection of various 5G waveforms by multi-user. The proposed filter includes three cascaded stages: cosine filtering, Welch segmentation, and Hamming windowing. The proposed detection system is applied on different data such as various of waveforms and SNRs. The simulation results exhibit a significant detection performance for the parameters of less than zero dB of SNR, greater than 95% global detection probability, less than 1% global system error probability, and less than 1% global false alarm probability. In addition, the detection performance reveals that the proposed system is outperformed related works as shown in the comparison in terms of the previous parameters. [ABSTRACT FROM AUTHOR]

Objectives: Respiratory infections are among the most common infectious diseases, resulting in significant morbidity and mortality. C-reactive protein (CRP), serum amyloid A (SAA), procalcitonin (PCT), and interleukin-6 (IL-6) are advantageous for diagnosing respiratory tract infections. This study assessed the analytical performance and accuracy of new kits for Eu3 + -based CRP/SAA and PCT/IL-6 lateral flow immunoassay and its diagnostic value in respiratory tract infections., Methods: This study evaluated the detection performance of a test kit using guidelines from the Center for Medical Device Evaluation (CMDE) and the Clinical and Laboratory Standards Institute (CLSI). The test results were compared to those of the commercial kits (CRP: Mindray; SAA: Norman; PCT: Shanghai Upper; IL-6: Wantai BioPharm). A total of 156 patients with respiratory tract infections (53 with bacterial infections (Bac group); 50 with viral infections (Vir group); and 53 with co-infections (Bac + Vir group)) were enrolled, along with 50 healthy controls (HC group). Venous blood samples were collected to measure levels of SAA, PCT, CRP, and IL-6 using both the test and commercial kits. The diagnostic value of these biomarkers was assessed using receiver operating characteristic (ROC) curves., Results: Correlation analysis demonstrated a strong concordance between the test kits and commercial kits (CRP: r = 0.9396, P < 0.0001; SAA: r = 0.8986, P < 0.0001; PCT: r = 0.9594, P < 0.0001; IL-6: r = 0.9009, P < 0.0001). The diagnostic performance of the test kits in identifying bacterial, viral, and co-infections was highly consistent with that of the commercial kit., Conclusions: The Eu3 + -based CRP/SAA and PCT/IL-6 lateral flow immunoassay test kits demonstrated high levels of consistency with commercial kits in terms of quantitative outcomes and diagnostic performance for respiratory tract infections., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

ABSTRACT The rapid and accurate diagnostic methods to identify carbapenemase-producing organisms (CPO) is of great importance for controlling the CPO infection. Herein, we have developed a microfluidic chip-based technique to detect CPO and assessed its clinical value in detecting CPO directly from blood cultures (BCs). The detection performance of the microfluidic chip-based LAMP amplification method was analyzed retrospectively on a collection of 192 isolates including molecularly characterized 108 CPO and 84 non-CPO and prospectively on a collection of 133 positive BCs with or without CPO suspicion, respectively. In the retrospective study, the microfluidic chip-based LAMP amplification method exhibited 87.5% accuracy (95% CI [82.0–91.5]), 97.7% sensitivity (95% CI [91.2–99.6]), 78.8% specificity (95% CI [69.5–86.0]), 79.6% positive predictive value (PPV) (95% CI [70.6–86.5]) and 97.6% negative predictive value (NPV) (95% CI [90.9–99.6]). Among the 192 isolates, 22 (11.5%) false-positives (FP) and 2 (1.0%) false negatives (FN) were observed. In the prospective study, the 133 routine isolates of positive BCs including 18 meropenem-resistant CPO and 115 non-CPO were assessed, and 4 FP were observed in non-CPO and CPO, respectively. The current method showed a total detection performance of 94.0% accuracy (95% CI [88.4–97.1]), 100.0% sensitivity (95% CI [73.2–100.0]), 93.2% specificity (95% CI [86.7–96.8]), 63.6% PPV (95% CI [40.8–82.0]) and 100.0% NPV (95% CI [95.8–100.0]). In summary, the microfluidic chip-based LAMP amplification method is reliable for the rapid screening and detection of CPO with high accuracy, sensitivity, and specificity, and could easily be implemented in clinical microbiology laboratories. IMPORTANCE Rapid and accurate identification of CPO may reduce the genetic exchanges among bacteria and prevent further dissemination of carbapenemases to non-CPO. The current method had designed microfluidic chip-based LAMP amplification method for multiplex detection of carbapenemase genes and evaluated the detection performance of the newly method. The current method can rapidly screen and detect CPO with high accuracy, sensitivity, and specificity, and could easily be implemented in clinical microbiology laboratories, as this will reduce the carbapenem resistance issues worldwide.

Post-earthquake robots can be used extensively to inspect and evaluate building damage for safety assessment. However, the surrounding environment and path for such robots are complex and unstable with unexpected obstacles. Thus, path planning for such robot is crucial to guarantee satisfactory inspection and evaluation while approaching the ideal position. To achieve this goal, we proposed a distributed small-step path planning method using modified reinforcement learning (MRL). Limited distance and 12 directions were gridly refined for the robot to move around. The small moving step ensures the path planning to be optimal in a neighboring safe region. The MRL updates the direction and adjusts the path to avoid unknown disturbances. After finding the best inspection angle, the camera on the robot can capture the picture clearly, thereby improving the detection capability. Furthermore, the corner point detection method of buildings was improved using the Harris algorithm to enhance the detection accuracy. An experimental simulation platform was established to verify the designed robot, path planning method, and overall detection performance. Based on the proposed evaluation index, the postearthquake building damage was inspected with high accuracy of up to 98%, i.e., 20% higher than traditional unplanned detection. The proposed robot can be used to explore unknown environments, especially in hazardous conditions unsuitable for humans. [ABSTRACT FROM AUTHOR]

In the conventional frequency diverse array (FDA) radar designs, generalized likelihood ratio test (GLRT) detection utilizes coherent pulses. However, the impacts of an FDA multiple-input multiple-output (FDA-MIMO) radar system for detection with incoherent pulses have not been systematically investigated. In this paper, we present an incoherent square-law detector to analyse the performance of both the coherent and non-coherent airborne FDA-MIMO radars in a Neyman-Pearson sense. Moreover, the closed-form expressions of an incoherent square-law detector for the FDA-MIMO radars are derived. For a coherent FDA-MIMO radar, the optimal performance is achieved at a high signal-to-noise ratio (SNR), whereas the superiority of a non-coherent FDA-MIMO radar in distinguishing range dependent targets is validated. The corresponding theoretical derivations are verified by the extensive numerical results to show an improved performance.

To overcome the influence of the daytime skylight background on long-distance optical detection, a new type of shearing interference detection system was proposed to improve the detection performance of the traditional detection system for finding dark objects such as dim stars during the daytime. This article focuses on the basic principle and mathematical model as well as the simulation and experimental research of the new type of shearing interference detection system. The comparison of the detection performance between this new-type detection system and the traditional system is also carried out in this article. The experimental results show that the detection performance of the new type of shearing interference detection system is significantly better than that of the traditional system, and the image signal-to-noise ratio of this new-type system (about 13.2) is much higher than that of the best result of the traditional detection system (about 5.1).

Light detection and ranging (LiDAR) is widely used in autonomous vehicles to obtain precise 3D information about surrounding road environments. However, under bad weather conditions, such as rain, snow, and fog, LiDAR-detection performance is reduced. This effect has hardly been verified in actual road environments. In this study, tests were conducted with different precipitation levels (10, 20, 30, and 40 mm/h) and fog visibilities (50, 100, and 150 m) on actual roads. Square test objects (60 × 60 cm2) made of retroreflective film, aluminum, steel, black sheet, and plastic, commonly used in Korean road traffic signs, were investigated. Number of point clouds (NPC) and intensity (reflection value of points) were selected as LiDAR performance indicators. These indicators decreased with deteriorating weather in order of light rain (10–20 mm/h), weak fog (

Principal component analysis (PCA) and its variants have been widely used for process monitoring and quality control. A key issue of PCA-related methods is to select an appropriate number of principal components (PCs). However, few approaches for component selection consider the monitoring performance, and they usually rely on prior fault information. This article develops an effective algorithm for dynamic component selection, which selects components for each sample based on a detection performance index, without need for prior fault information. Component selection is transformed into a stochastic optimization problem, whose optimal solution is derived analytically. Then, a subset of components that are sensitive to faults is obtained. The proposed method reduces the requirement for the detectable fault amplitude, which leads to better performance. Furthermore, the differences between PCA and the proposed method are discussed, and online computational complexity is analyzed. Case studies on a continuous stirred tank heater (CSTH), the Tennessee Eastman process (TEP), and a real ultra-supercritical power plant demonstrate that the proposed method has better monitoring performance than PCA and some of its variants. [ABSTRACT FROM AUTHOR]

We present an overview of the detection of point scatterers in ultrasound images and suggest strategies for evaluating and measuring the detection performance. We use synthetic aperture Field II simulations of a point scatterer in speckle background and evaluate how common imaging techniques affect point target detectability. We discuss how to compare different methods and calculate confidence intervals. In general, applying speckle reduction methods reduces the point detection performance. However, the results show that it is possible to smooth the speckle background and preserve relatively high performance with a suitable and optimized method. The different detection performances of the advanced beamforming methods coherence factor (CF), phase coherence factor (PCF), and Capon’s minimum variance (MV) are presented and benchmarked with standard delay-and-sum (DAS). The results show that CF achieves slightly better detection performance than DAS for weak point scatterers, whereas PCF and MV perform worse than DAS. Choice of apodization window and adaptive aperture size affects the probability of detection. Results show that methods that preserve spatial resolution have better detection performance of point scatterers. [ABSTRACT FROM AUTHOR]

Visible light enters our body via the pupil. By changing its size, the pupil shapes visual input. Small apertures increase the resolution of high spatial frequencies, thus allowing discrimination of fine details. Large apertures, in contrast, provide a better signal-to-noise ratio, because more light can enter the eye. This should lead to better detection performance of peripheral stimuli. Experiment 1 shows that the effect can reliably be demonstrated even in a less controlled online setting. In Experiment 2, pupil size was measured in a laboratory using an eye tracker. The findings replicate findings showing that large pupils provide an advantage for peripheral detection of faint stimuli. Moreover, not only pupil size during information intake in the current trial n, but also its interaction with pupil size preceding information intake, i.e., in trial n-1, predicted performance. This suggests that in addition to absolute pupil size, the extent of pupillary change provides a mechanism to modulate perceptual functions. The results are discussed in terms of low-level sensory as well as higher-level arousal-driven changes in stimulus processing. [ABSTRACT FROM AUTHOR]

Leakage detection plays a significant role in water pipeline since leakage problems severely affect urban water supply safety. The fibre optic hydrophone derives from fibre optic hydroacoustic sensing technology and exhibits superior performance to traditional electric hydrophones. To investigate leakage detection performance of the proposed fibre optic hydrophone system, tests were conducted on overhead and buried pipelines, respectively. Two types of spherical valves were adopted for simulating leakage with different sizes. Effects of surrounding medium on leakage acoustic wave and positioning accuracy were studied. The signal was acquired by hydrophone analyser and analysed by a self-developed program. A revised cross-correlation analysis of multi-signals was proposed and compared with that of two signals; it presented a better performance in time delay estimation. With leakage, overhead pipelines showed larger amplitudes than that of buried pipelines and signal amplitude increased with the opening degree of valves. The results reveal that the surrounding medium of the pipeline affected the leakage wave velocity and could attenuate the wave. The cross-correlation analysis of multiple hydrophones could increase the accuracy of leakage positioning. The proposed leakage detection system was feasible to detect leakage of both overhead and buried pipelines. [ABSTRACT FROM AUTHOR]

Signal modulation is an essential design factor for proximity detectors and directly affects the system’s potential performance. In order to achieve the advantages of chaotic codes bi-phase modulation (CCBPM) and linear frequency modulation (LFM) simultaneously, this paper designed a waveform which combined chaotic codes bi-phase modulation and linear frequency modulation (CCBPM–LFM) for proximity detectors. The CCBPM–LFM waveform was analyzed in the aspect of time delay resolution (TDR) and Doppler tolerance (DT) based on ambiguity function (AF). Then, a ranging method, which we called instant correlation harmonic demodulation (ICHD), was presented for the detector using the CCBPM–LFM waveform. By combining time domain instant correlation with harmonic demodulation, the ICHD solved the problem caused by combination modulation and made the most of the linear frequency modulation (LFM) harmonics and the correlation of chaotic codes. Finally, a prototype was implemented and ranging experiments were carried out. From the theoretical analysis and experimental results, the proximity detector used the CCBPM–LFM waveform has an outstanding detection performance.

High-efficiency image corner detection, one of the most important and critical basic technology in industrial image processing, is to detect point features from an input image in real-time. In this article, we propose a new corner detection method which has both good performance of corner detection and real-time processing abilities. Firstly, the integral image and the box filter are combined to obtain the second-order derivative response in each direction of the image. Secondly, a new coarse screening mechanism for candidate corners is presented to reduce the complexity of the corner metric. Thirdly, a non-maximum suppression operation is utilized to obtain corners. Finally, the performance evaluation on accuracy of corner detection, localization error, average repeatability, region repeatability, different lighting conditions, and execution time are used to assess the proposed method against twelve state-of-the-art methods. The experimental results show that our proposed detector has good corner detection performance and achieves the requirement of real-time processing.

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For millennia, people have used "averted vision" to improve their detection of faint celestial objects, a technique first documented around 325 BCE. Yet, no studies have assessed gaze location during averted vision to determine what pattern best facilitates perception. Here, we characterized averted vision while recording eye‐positions of dark‐adapted human participants, for the first time. We simulated stars of apparent magnitudes 3.3 and 3.5, matching their brightness to Megrez (the dimmest star in the Big Dipper) and Tau Ceti. Participants indicated whether each star was visible from a series of fixation locations, providing a comprehensive map of detection performance in all directions. Contrary to prior predictions, maximum detection was first achieved at ~8° from the star, much closer to the fovea than expected from rod‐cone distributions alone. These findings challenge the assumption of optimal detection at the rod density peak and provide the first systematic assessment of an age‐old facet of human vision. [ABSTRACT FROM AUTHOR]

Bisphenol A (BPA) is widely used in the manufacture of epoxy, polycarbonate plastics, flame retardants, cosmetics and even baby products. However, as an endocrine disruptor, it can lead to metabolic and reproductive disorders. In severe cases, it can cause various cancers. Among all kinds of BlPA detection techniques, electrochemical methods are paid more attention because of their advantages such as simple operation, fast response time, high sensitivity, low cost and portable instrument. In this review, the research progress of the modified electrode's composite materials of electrochemical sensor for BPA detection in the past 5 years and its application in the analysis of real samples are summarized. The preparation of electrode modified composite materials and the performance of the electrochemical sensors for BPA detection are emphasized so as to provide reference and new ideas for its future development and practical application. [ABSTRACT FROM AUTHOR]

In the conventional frequency diverse array (FDA) radar designs, generalized likelihood ratio test (GLRT) detection utilizes coherent pulses. However, the impacts of an FDA multiple-input multiple-output (FDAMIMO) radar system for detection with incoherent pulses have not been systematically investigated. In this paper, we present an incoherent square-law detector to analyse the performance of both the coherent and non-coherent airborne FDA-MIMO radars in a Neyman-Pearson sense. Moreover, the closed-form expressions of an incoherent square-law detector for the FDA-MIMO radars are derived. For a coherent FDA-MIMO radar, the optimal performance is achieved at a high signal-to-noise ratio (SNR), whereas the superiority of a non-coherent FDA-MIMO radar in distinguishing range dependent targets is validated. The corresponding theoretical derivations are verified by the extensive numerical results to show an improved performance. [ABSTRACT FROM AUTHOR]

Biosensors have been proven to be crucial in the rapid detection of food. However, food is rich in various small molecules such as proteins, lipids, and carbohydrates, which can occupy the binding sites on the biosensor's surface, significantly diminishing its detection performance. Therefore, there is an urgent need to develop a method that can enhance the detection performance of biosensors in complex food matrices without requiring complex pretreatment processes. Zwitterions, as a type of anti-fouling material, have gained significant attention because of their ability to enhance biosensor detection performance in complex matrices. This review provides a detailed introduction to the design and anti-fouling principles of zwitterionic polymers, surface polymerization and immobilization, and the applications of zwitterionic polymers in rapid detection, aiming to provide new insights and methods for improving the detection performance of biosensors in complex matrices. In an environment where biosensors are deployed for detection, proper pH balance and ionic strength are essential prerequisites for achieving optimal performance. Considering that zwitterions respond to both pH and salt levels, striking a balance between noise reduction and sensitization is particularly important. In addition, we conducted a detailed analysis on the impact of the coating thickness of zwitterionic polymers on the detection performance of biosensors. [Display omitted] • The structural design of zwitterionic polymers and their immobilization strategies for biosensors are reviewed. • The factors affecting the pH and salt response of zwitterions are proposed. • The anti-fouling mechanism of zwitterions are discussed. • Noting the research progress on zwitterions-modified biosensors are proposed. • The advantages and disadvantages of zwitterions are summarized, and the trends in zwitterions-modified biosensors are prospected. [ABSTRACT FROM AUTHOR]

Abstract We present systematic investigations on the physics, detection performance and inversion of logging-while-drilling extra-deep azimuthal resistivity measurements (EDARM). First, the definitions of EDRAM measurements are discussed, followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals. Then, a new definition of detection depth, which considers the uncertainty of inversion results caused by the data noise, is proposed to quantify the detection capability of EDARM. Finally, the Bayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data. Numerical results show that EDARM is capable of detecting the azimuth and distance of remote bed boundaries, and the detection capability increases with increasing spacing and resistivity contrast. The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles. In addition, multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.