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Detecting highly-overlapped objects in crowded scenes remains a challenging problem, especially for one-stage detector. In this paper, we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme, named YOLO-CS. Specifically, we give YOLOv4 the power to detect multiple objects in one cell. Center to our method is the carefully designed joint prediction scheme, which is executed through an assignment of bounding boxes and a joint loss. Equipped with the derived joint-object augmentation (DJA), refined regression loss (RL) and Score-NMS (SN), YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time. Furthermore, on the widely used general benchmark COCO, YOLO-CS still has a good performance, indicating its robustness to various scenes.

The challenging problem in the traffic system is lane detection. This Lane detection which attracts the computer vision community’s attention. For computer vision and machine learning techniques, the Lane detection which acts as the multi-feature detection problem. Many machine learning techniques are used for lane detection. Driver support system is one of the most important features in the modern vehicles to ensure the safety of the driver and decrease the vehicle accidents on road. Road Lane detection is the most challenging task and complex tasks now-a-days. Road localization and relative position between vehicle and roads which also includes with this. But in this journal, we propose a new method. Here, an on- board camera to be used which is looking outwards are presented here in this work. This proposed technique which can be used for all types of roads like painted, unpainted, curved, straight roads etc in different weather conditions. No need for camera calibration and coordination of the transform, may be any changing illumination situation, shadow effects, various road types. No representation for speed limits. This includes that the system acquires the front view using a camera mounted on the vehicles and detect the Lane by applying the code from the Python Programming process. This proposed system does not require any more information about roads. This system which demonstrates a robust performance for Lane detection.

Microstrip silicon detectors are one of the key trackers in particle physics experiment like LHC at CERN. There is a continuous need to improve upon various parameters of detectors like breakdown voltage, leakage current, full depletion voltage, post radiation hardness, charge collection efficiency and low thermal budget. We have introduced new doping less silicon microstrip detector for the very first time and investigated the various parameter of the detector. We have studied and compared the results of doped and dopingless charge plasma silicon microstrip detector. Our study shows improvement in Preradiation breakdown voltage of 2200 V with leakage current of 5.2 pA/um in dopingless charge plasma microstrip detector as compared to 1500 V of breakdown voltage with leakage current of 6.5pA/um as in the case of doped silicon microstrip detector.

The fast and high response detection of neurotoxic H2S is of great importance for the environment. In this paper, directly electrospinning technology on the ceramic tube is developed to improve the response of H2S detector based on superlong SnO2 fibers. The submillimeter continuous fibers are deposited directly on ceramic tubes by in-situ electrospinning method and can keep morphology of fibers during calcination. By employing this technology, CuO-doped SnO2 fiber H2S detectors are fabricated, and 10% atom CuO-doped SnO2 H2S detector shows the highest response of 40 toward 1 ​ppm ​H2S at 150 ​°C while the response is only 3.6 for the H2S detector prepared in traditional route. In addition, the in-situ electrospinning H2S detectors show faster response and recovery compared to the H2S detectors fabricated by the conventional way. The high and fast response of H2S detectors based on in-situ electrospinning can be ascribed to the continuous fiber structure and CuO modification. The present in-situ electrospinning technology may provide a new strategy for the development of other gas-detectors and bio-detectors with fast and high response.

The purpose of this comprehensive research is to observe the impact of scintillator crystal type on entire detection process. For this aim, MCNPX (version 2.6.0) is used for designing of a physical plastic scintillation detector available in our laboratory. The modelled detector structure is validated using previous studies in the literature. Next, different types of plastic scintillation crystals were assessed in the same geometry. Several fundamental detector properties are determined for six different plastic scintillation crystals. Additionally, the deposited energy quantities were computed using the MCNPX code. Although six scintillation crystals have comparable compositions, the findings clearly indicate that the crystal composed of PVT 80% + PPO 20% has superior counting and detecting characteristics when compared to the other crystals investigated. Moreover, it is observed that the highest deposited energy amount, which is a result of the highest collision number in the crystal volume, corresponds to a PVT 80% + PPO 20% crystal. Despite the fact that plastic detector crystals have similar chemical structures, this study found that performing advanced Monte Carlo simulations on the detection discrepancies within the structures can aid in the development of the most effective spectroscopy procedures by ensuring maximum efficiency prior to and during use.

Deep learning-based methods have achieved remarkable success in object detection, but this success requires the availability of a large number of training images. Collecting sufficient training images is difficult in detecting damages of airplane engines. Directly augmenting images by rotation, flipping, and random cropping cannot further improve the generalization ability of existing deep models. We propose an interactive augmentation method for airplane engine damage images using a prior-guided GAN to augment training images. Our method can generate many types of damages on arbitrary image regions according to the strokes of users. The proposed model consists of a prior network and a GAN. The Prior network generates a shape prior vector, which is used to encode the information of user strokes. The GAN takes the shape prior vector and random noise vectors to generate candidate damages. Final damages are pasted on the given positions of background images with an improved Poisson fusion. We compare the proposed method with traditional data augmentation methods by training airplane engine damage detectors with state-of-the-art object detectors, namely, Mask R-CNN, SSD, and YOLO v5. Experimental results show that training with images generated by our proposed data augmentation method achieves a better detection performance than that by traditional data augmentation methods.

Small-object detection has long been a challenge. High-megapixel cameras are used to solve this problem in industries. However, current detectors are inefficient for high-resolution images. In this work, we propose a new module called Pre-Locate Net, which is a plug-and-play structure that can be combined with most popular detectors. We inspire the use of classification ideas to obtain candidate regions in images, greatly reducing the amount of calculation, and thus achieving rapid detection in high-resolution images. Pre-Locate Net mainly includes two parts, candidate region classification and behavior classification. Candidate region classification is used to obtain a candidate region, and behavior classification is used to estimate the scale of an object. Different follow-up processing is adopted according to different scales to balance the variance of the network input. Different from the popular candidate region generation method, we abandon the idea of regression of a bounding box and adopt the concept of classification, so as to realize the prediction of a candidate region in the shallow network. We build a high-resolution dataset of aircraft and landing gears covering complex scenes to verify the effectiveness of our method. Compared to state-of-the-art detectors (e.g., Guided Anchoring, Libra-RCNN, and FASF), our method achieves the best mAP of 94.5 on 1920 × 1080 images at 16.7 FPS.

The performance of traffic signal phasing and timing (SPaT) plans are directly related to temporal fluctuations in traffic volumes and their distribution across competing movements at the intersection. A well-timed signal plan generally allocates green times in proportion to the observed volumes. With the advance of data collection techniques and technology, it is possible to obtain and use high-resolution traffic data using detectors at intersections to measure the real-time performance of the SPaT plan to better achieve this goal. This study introduces novel green time metrics and methods to evaluate the efficiency of green time allocation along arterials and at individual intersections using automated, real-time, and high-resolution traffic data. An empirical application is presented for four intersections along a major radial arterial in Salt Lake City, Utah and a detailed analysis of the phases and movements for a single intersection located on that arterial. The proposed methods and results can help identify opportunities to improve the signal timing plan for the entire corridor or movements within secondary approaches by identifying locations and/or time periods where signal timing does not align well with observed traffic volumes. These metrics can be automated to provide traffic engineers with an alert as to when a particular intersection should be examined as a candidate for signal retiming, and they can also help identify what changes need to be made with the current SPaT plans.

This article focused on the emission probability of the grey particles with black particles emitted in the interaction of 84Kr (as a projectile) having incident kinetic energy 1 GeV per nucleon with nuclear emulsion detector (as target detector). This study show that the emission probability of grey particles is depending on the number of emitted black particles.

In this manuscript we have focused on the shower particle characteristics produced at relativistics energy. We have also investigated the inequality of the shower particles emitted out of the interactivity of the projectiles with discrete target nuclei of the emulsion detector. In this investigation we have remarked that as we move to heavier target nuclei the release probability of shower particles is increases.

The nuclear charge pickup cross sections of 40Ar on polyethylene(CH2), carbon, aluminum, copper and lead targets at the highest energy of 495 MeV/n were measured using CR-39 nuclear track detector. The cross sections for H were calculated from those measured on C and CH2 targets. The dependence of charge pickup cross section on target mass was studied, it was found that the nuclear charge pickup cross section exhibits a linear dependence on the target mass.

The activity of gamma-ray emitting nuclides is calculated assuming that each gamma-ray is detected individually; thus, the magnitude of the coincidence summing signal must be considered during activity calculations. Here, the correction factor for the coincidence summing effect was calculated, and the detection efficiencies of two HPGe detectors were compared. The CANBERRA Inc. GC4018 high-purity Ge detector provided an estimate for the peak-to-total ratio using a point source to determine the coincidence summing correction factor. The ORTEC Inc. GEM60 high-purity Ge detector uses EFFTRAN in LVis to obtain the parameters of the detector and source model and the gamma-gamma and gamma-X match estimates, in order to determine the coincidence summing correction factor. Nuclide analyses, radioactivity comparisons, and analyses of reference material samples were performed utilizing certified reference materials to accurately determine the detection efficiencies. For both Co-60 and Y-88, the detection efficiency for a point source increased by an average of at least 12–13%, whereas the detection efficiency determined using LVis increased by an average of at least 13–15%. The calculated radioactivity values of the certified reference material and reference material samples were accurate to within 3% and 6% of the measured values, respectively.

The development of ultra-high-resolution CT (U-HRCT) is expected to improve the accuracy of coronary stenosis evaluation. This study aimed to evaluate the accuracy of the stenosis severities of coronary artery phantoms estimated using U-HRCT by comparing them to those estimated with conventional CT.Coronary artery phantoms with non-calcified and calcified lesions were scanned with conventional CT (64-row ​× ​0.625 ​mm) and U-HRCT (32-row ​× ​0.3125 ​mm). The coronary artery phantoms had lumen diameters of 2.0, 3.0, and 4.0 ​mm with non-calcified lesions representing 0%, 25%, 50%, and 75% stenosis and 3.0 and 4.0 ​mm with calcified lesions representing 0%, 25%, 50%, and 75% stenosis. The lumen diameters at the stenotic and non-stenotic regions were measured, and the stenosis severities were compared with the true values.For non-calcified lesions, conventional CT significantly underestimated the stenosis severity in the phantom showing 75% stenosis with lumen diameters of 2.0 and 3.0 ​mm (p ​ ​0.05), while the estimated stenosis severities were not significantly different from the true values at all settings with U-HRCT. For the calcified lesions, conventional CT overestimated the stenosis severities at all settings (p ​ ​0.05), while U-HRCT yielded estimations closer to the true values, although still with some overestimation (p ​ ​0.05).By using U-HRCT, the estimated stenosis severities of the coronary artery with non-calcified lesion become almost equal to the true value, while those with calcified lesion are still overestimated although they become closer to the true value.

Compton imaging is the main method for locating radioactive hot spots emitting high-energy gamma-ray photons. In particular, this imaging method is crucial when the photon energy is too high for coded-mask aperture imaging methods to be effective or when a large field of view is required. Reconstruction of the photon source requires advanced Compton event processing algorithms to determine the exact position of the source. In this study, we introduce a novel method based on a Deep Learning algorithm with a Convolutional Neural Network (CNN) to perform Compton imaging. This algorithm is trained on simulated data and tested on real data acquired with Caliste, a single planar CdTe pixelated detector. We show that performance in terms of source location accuracy is equivalent to state-of-the-art algorithms, while computation time is significantly reduced and sensitivity is improved by a factor of ∼5 in the Caliste configuration.

International audience; Purpose: This study is about the development of a new dual wavelength reading method of Fricke-Xylenol orange-Gelatin (FXG) gel dosimeters on the Vista16™ optical Computed Tomography (CT) scanner to perform 3D dose distribution measurements in stereotactic and dynamic radiotherapy treatments.Methods: The dosimetric characteristics of an optimized FXG gel composition and its optical CT readout have been evaluated. A dual wavelength reading method has been developed on the CT scanner at wavelengths 590 nm and 633 nm. Small-field dose profile measurements with FXG gel and microDiamond (PTW) detectors were compared by γ-index analysis (0.5%/0.5mm) to validate this method. Results: This new reading method exhibits linear calibration curves in the 0 - 4 Gy and 2 - 10 Gy dose ranges at 590 nm and 633 nm respectively. The absorbed dose values under 4 Gy, measured at 590 nm, and those above 4 Gy, measured at 633 nm, are combined to plot a complete profile. A γ passing rate of 93.4 % was achieved.Conclusions: The new reading method of FXG gel dosimeters has been successfully implemented on the Vista16™ scanner to span absorbed doses representative of stereotactic and dynamic radiotherapy treatments and enable 3D measurements in tumor volumes and surrounding healthy tissues. Small-field profile measurements validated this reading method as FXG gel dosimeters and microDiamond detectors were in very close agreement. This dosimetric method is a promising candidate for 3D quality assurance end-to-end tests in stereotactic and dynamic radiotherapy.

With the rapid development of microwave photonics technology, high-speed processing and ultra-weak signal detection capability have become the main bottlenecks in many applications. Thanks to the ultra-weak signal detection capability and the extremely low timing jitter properties of single-photon detectors, the combination of single-photon detection and classical microwave photonics technology may provide a solution to break the above bottlenecks. In this paper, we first report a novel concept of single-photon microwave photonics (SP-MWP), a SP-MWP signal processing system with phase shifting and frequency filtering functionalities is demonstrated based on a superconducting nanowire single photon detector (SNSPD) and a successive time-correlated single photon counting (TCSPC) module. Experimental results show that an ultrahigh optical sensitivity down to −100 dBm has been achieved, and the signal processing bandwidth is only limited by the timing jitter of single-photon detectors. In the meantime, the proposed system demonstrates an ultrahigh anti-interference capability, only the signal which is phase locked by the trigger signal in TCSPC can be extracted from the detected signals combining with noise and strong interference. The proposed SP-MWP concept paves a way to a novel interdisciplinary field of microwave photonics and quantum mechanism, named by quantum microwave photonics.

The aim of this work is to study the radiation shielding properties of cement samples with waste glass incortated into its composition. The mass attenuation coefficient (MAC) of the samples were experimentally determined to evaluate their radiation shielding ability. The experimental coefficient was evaluated using NaI detector for gamma energies between 59.53 keV and 1408.01 keV using different radioactive point sources Am-241, Eu-152, Co-60, and Cs-137, and the gamma transmission parameters half-value layer, mean free path, and transmission factor were calculated. The theoretical coefficient of the composites was determined using Geant4 and XCOM software. The results were also compared against Geant4 and XCOM simulations by calculating the relative deviation between the values to determine the accuracy of the results. In addition the mechanical properties (including Compressive and porosity) as well as the thermogravimetric analysis were tested for the present samples. Overall, it was concluded that the cement sample with 50% waste glass has the greatest shielding potential for radiation shielding applications and is a useful way to reuse waste glass.

Multi-element array photoelectric detector is the core devices to form a photoelectric detection target with a large field of view. This photoelectric detection target brings about the problem of uneven detection sensitivity distribution in the detection screen. To improve the uneven detection sensitivity of this photoelectric detection target, this paper analyzes the distribution law of the uneven detection sensitivity of the photoelectric detection target using the multi-element array photoelectric detector, dissects the main factors affecting the detection sensitivity according to the photoelectric detection principle, establishes the calculation model of detection sensitivity of the photoelectric detection target in the different detection areas and proposes a method to improve the detection sensitivity by compensating the gain of each unit photoelectric detector. The analysis of simulation and experimental results show that the proposed method of photoelectric detection target can effectively improve the output signal amplitude of the projectile under the certain detection distance, in particular, the output signal amplitude of the projectile is significantly increased when the projectile passes through the detection blind area. The experimental results are consistent with the simulation results, which verify the effectiveness of the proposed improvement method.

With the increasing demand for surveillance applications, pedestrian detection has been a topic of interest for many researchers in recent time. The quality of a pedestrian detector is decided in terms of detection accuracy and rate of detection. This paper presents new pedestrian detectors based on two types of classifiers, linear support vector machine and cascade of boosted classifier. These classifiers are trained by using a feature set comprising of the histogram of oriented gradients and dense local difference binary features. Both the image pyramid and non-linear scale space are used to detect pedestrians of various sizes. In order to combine the benefits of the two classifiers, a new two-stage detection scheme is also presented. The detection accuracies of the proposed detectors are studied in terms of miss-rate versus false positive per image and miss-rate versus false positive per window. The performances of the detectors are also compared with the performances of existing detectors of similar type.

In this study, a diamond-based neutron scatter camera (DNSC) was developed for neutron spectroscopy in high flux environments. The DNSC was evaluated experimentally and through simulations. It was simulated using several Monte Carlo codes in a two-array layout. The two-array model included two diamond detectors. The simulation reconstructed the spectra of 252Cf and 239Pu–Be neutron sources with high accuracy (∼93%). The two-diamond array system was experimentally evaluated, demonstrating the neutron spectroscopy capabilities of the DNSC. The reconstructed spectrum of the 239Pu–Be source manifested the characteristic peaks of the source. The advantage of a DNSC over a NSC is its ability to define any neutron double-scattering events without the need to absorb incident neutrons in the second detector, and atomic recoil energy information is not needed to determine the incident neutron energy.

The computation of the solid angle and the detector efficiency is considering to be one of the most important factors during the measuring process for the radioactivity, especially the cylindrical γ-ray NaI(Tl) detectors nowadays have applications in several fields such as industry, hazardous for health, the gamma-ray radiation detectors grow to be the main essential instruments in radiation protection sector. In the present work, a generic numerical simulation method (NSM) for calculating the efficiency of the γ-ray spectrometry setup is established. The formulas are suitable for any type of source-to-detector shape and can be valuable to determine the full-energy peak and the total efficiencies and P/T ratio of cylindrical γ-ray NaI(Tl) detector setup concerning the truncated conical radioactive source. This methodology is based on estimate the path length of γ-ray radiation inside the detector active medium, inside the source itself, and the self-attenuation correction factors, which typically use to correct the sample attenuation of the original geometry source. The calculations can be completed in general by using extra reasonable and complicate analytical and numerical techniques than the standard models; especially the effective solid angle, and the detector efficiency have to be calculated in case of the truncated conical radioactive source studied condition. Moreover, the (NSM) can be used for the straight calculations of the γ-ray detector efficiency after the computation of improvement that need in the case of γ-γ coincidence summing (CS). The (NSM) confirmation of the development created by the efficiency transfer method has been achieved by comparing the results of the measuring truncated conical radioactive source with certified nuclide activities with the γ-ray NaI(Tl) detector, and a good agreement was obtained after corrections of (CS). The methodology can be unlimited to find the theoretical efficiencies and modifications equivalent to any geometry by essential sufficiently the physical selective considered situation.

KHNP had licensed Dynamic Control rod Reactivity Measurement (DCRM) method using detector current signals of PWRs in 2006. The method has been applied to all PWRs in Korea for about 15 years successfully. However, the original method was inapplicable to PWRs using low-sensitivity integral fission chamber as ex-core detectors because of their pulse pile-up and the nonlinearity of the mean-square voltage at low power region. Therefore, to overcome this disadvantage, a modified method, DCRM-EK, was developed using kinetics behavior after equilibrium condition where the pulse counts maintain the maximum value before pulse pile-up. Overall measurement, analysis procedure, and related computer codes were changed slightly to reflect the site test condition. The new method was applied to a total of 15 control rods of 1000 MWe and 1400 MWe PWRs in Korea with worths in the range of 200 pcm–1200 pcm. The results show the average difference of -0.4% and the maximum difference of 7.1% compared to the design values. Therefore, the new DCRM-EK will be applied to PWRs using low sensitivity integral fission chambers, and also can replace the original DCRM when the evaluation fails by big noises present in current or voltage signals of uncompensated/compensated ion chambers.

A high detection efficiency thermal neutron detector based on the 6LiF/ZnS(Ag) scintillation screens, wavelength-shifting fibers (WLSF) and Silicon photomultiplier (SiPM) readout is under development at China Spallation Neutron Source (CSNS) for the Engineering Material Diffractometer (EMD).A prototype with a sensitive volume of 180mm×192mm has been built. Signals from SiPMs are processed by the self-design Application Specific Integrated Circuit (ASIC). The performances of this detector prototype are as follows: neutron detection efficiency could reach 50.5% at 1 A, position resolution of 3, the dark count rate 200KHz. Such detector prototype could be an elementary unit for applications in the EMD detector arrays.

International audience; Proton computed tomography (pCT) is a promising tomographic imaging modality allowing direct reconstruction of proton relative stopping power (RSP) required for proton therapy dose calculation. In this review article, we aim at highlighting the role of Monte Carlo (MC) simulation in pCT studies. After describing the requirements for performing proton computed tomography and the various pCT scanners actively used in recent research projects, we present an overview of available MC simulation platforms. The use of MC simulations in the scope of investigations of image reconstruction, and for the evaluation of optimal RSP accuracy, precision and spatial resolution omitting detector effects is then described. In the final sections of the review article, we present specific applications of realistic Monte Carlo simulations of an existing pCT scanner prototype, which we describe in detail.

Purpose Proton computed (transmission) tomography (pCT) refers to the process of imaging an object by letting protons pass through it, while measuring their energy after, and their position and (optionally) direction both before and after their traversal through that object. The so far experimental technique has potential to improve treatment planning of proton therapy by enabling the direct acquisition of a proton stopping power map of tissue, thus removing the need to obtain it by converting X-ray CT attenuation data and thereby eliminating uncertainties which arise in the mentioned conversion process. The image reconstruction in pCT requires accurate estimates of the proton trajectories. In experimental pCT detector setups where the direction of the protons is not measured, the air gap between the detector planes and the imaged object worsens the spatial resolution of the image obtained. In this work we determined the mean proton paths and the corresponding spatial uncertainty, taking into account the presence of the air gap. Methods We used Monte Carlo simulations of radiation transport to systematically investigate the effect of the air gap size between detector and patient on the spatial resolution of proton (ion) computed tomography for protons with an energy of 200 MeV and 250 MeV as well as for helium ions (He-4) with an energy of 798 MeV. For the simulations we used TOPAS which itself is based on Geant4. Results For all particles, which are detected at the same entrance and exit coordinate, the average ion path and the corresponding standard deviation was computed. From this information, the dependence of the spatial resolution on the air gap size and the angular confusion of the particle beam was inferred. Conclusion The presence of the airgap does not pose a problem for perfect fan beams. In realistic scenarios, where the initial angular confusion is around 5 mrad and for typical air gap sizes up to 10 cm, using an energy of 200 MeV a spatial resolution of about 1.6 mm can be achieved. Using protons with E = 250 MeV a spatial resolution of about 1.1 mm and using helium ions (He-4) with E = 798 MeV even a spatial resolution below 0.7 mm respectively is attainable.

Metal-organic frameworks with structures including metal ions and organic connectors have shown vast applications like catalysis, gas storage, drug delivery, and detection. In this review, applications in the detection of metal ions, explosives, pesticides, and antibiotics by luminescent metal–organic frameworks is reviewed. Metal-organic frameworks have diverse topology, pores of various sizes and shapes, vast surface area, and high number of active sites which make them more suitable for chemical detectors as compared to traditional detectors. The spectrum of this review covers antibiotics detection because their misuse has significant environmental and health consequences. This review also covers pesticide residues detection which are toxic for organisms as well as the environment. Metal contamination is another major problem, their detection is very much required and is also addressed in this review. Luminescent metal–organic framework (LMOFs) were reported to be materials for detecting various explosive chemicals. The ease of use, adaptability and tunable photoluminescence capabilities make them excellent detectors.

The polar solvent concentration identification or alcohol presence finder or poisons detection and concentration finding are the most needed evaluation methods in crime investigation and hospital cases. This sophisticated method of approach compensates with the usability cost, the complexity of identification, the accuracy of data, instrument inabilities, easy production, and compatibility of all working conditions. Existing analyzers are with less features and less functionality. The accuracy in sensitivity and response time are the two variable in our system got much attention. Our design and developmental approach for the development of polar chemical sensors may overcome the exciting technologies drawback. The alcohol detector, with its specified concentration level representation, will give us broad applicability in all level peoples. Femtosecond laser-assisted machined functional superhydrophobic surfaces are the very innovative and new approach to this proposed device. We are exploring the laser modified superhydrophobic surface with polar solvent chemical sensing ability by contact angle variation incorporated portable device.

The study of refractive index provides the complete picture of material bodies with reference to their major properties like physical, chemical, optical, etc., and hence decides the behavior of the material body at macro level. Measurement of refractive index in small volume samples using a U-shaped extrinsic fiber optic sensor based on the principle of direct contact evanescent wave absorption can be employed to detect such information. In order to obtain such measurements, a U-shaped glass probe connected between a light transmitter and an optical detector using two PCS multimode optical fibers of the same diameters (200/230 μm) was developed and investigated in the present study. Liquid mixtures prepared using Toluene and Acetic acid taking at different proportions making total volume equivalent to 20 ml employing a two burette system. When the liquid mixtures are applied on the U-shaped probe the light reaching the detector from the source was shown varying depending upon the refractive index of the mixture. The changes in the transmitted power spectra were measured as a function of absorption of higher order evanescent wave modes at various refractive index values at different temperature levels ranging from 20 °C to 60 °C of the mixture and by repeating the experiment with various operating wavelengths (630 nm, 660 nm, 820 nm & 850 nm). The calibrated curves were plotted, which can be used to determine the refractive index of liquids at various temperatures with high degree of accuracy, precision and sensitivity measurements, exploiting all the benefits offered by the optical fiber communication systems.

The transmission of the high data rate and information over the optical fiber is restricted by various dispersion phenomenon which further results into inter-symbol interference (ISI). There are numerous alternatives present to resolve this hurdle of dispersion. Unlike other dispersion compensating procedures, electronic dispersion compensators (EDC) which makes use of non-linear and linear equalization approach is an effectual, cost effective and compact dispersion compensating method. This paper is approaching EDC technique for compensation of chromatic dispersion at four distinct bit rates of 25, 30, 40 and 40 Gbps over 120Km of single mode fiber. To Achieve this, a single channel optical system with the help of simulation software i.e., Optisystem is being used for the above-mentioned technique. After exploring their outcomes, another operation of comparing these two-bit rates using pin photodiode and avalanche photodiode has been applied. The simulations showed the optimum photodiode for efficacious dispersion compensation at such a long transmission distance.

Commercialization of self-driving applications requires precision and reliability of the perception system due to the highly dynamic and complex road environment. Early perception systems either rely on the camera or on LiDAR for moving obstacle detection. With the development of vehicular sensors and deep learning technologies, the multi-view and sensor fusion based convolutional neural network (CNN) model for detection tasks has become a popular research area. In this paper, we present a novel multi-sensor fusion-based CNN model–SaccadeFork–that integrates the image and upsampled LiDAR point clouds as the input. SaccadeFork includes two modules: (1) a lightweight backbone that consists of hourglass convolution feature extraction module and a parallel dilation convolution module for adaptation of the system to different target sizes; (2) an anchor-based detection head. The model also considers deployment of resource-limited edge devices in the vehicle. Two refinement strategies, i.e., Mixup and Swish activation function are also adopted to improve the model. Comparison with a series of latest models on public dataset of KITTI shows that SaccadeFork can achieve the optimal detection accuracy on vehicles and pedestrians under different scenarios. The final model is also deployed and tested on a local dataset collected based on edge devices and low-cost sensor solutions, and the results show that the model can achieve real-time efficiency and high detection accuracy.

Artificial intelligence (AI) has been widely used throughout medical imaging, including PET, for data correction, image reconstruction, and image processing tasks. However, there are number of opportunities for the application of AI in photon detector performance or the data collection process, such as to improve detector spatial resolution, time-of-flight information, or other PET system performance characteristics. This review outlines current topics, research highlights, and future directions of AI in PET instrumentation.

Pedestrian detection is a common task in the research area of video analysis and its results lay the foundations of a wide range of applications. It is commonly known that under challenging illumination and weather conditions, conventional visible cameras perform poorly and this limitation could be catered using thermal imagery. But, due to the fact that annotated thermal datasets are less available compared to the visible ones, in this paper we emphasis the need for leveraging information from the visible domain to perform detection in the thermal domain at no additional annotation cost. Precisely, we propose a domain adaptation method by incorporating feature distribution alignments into Faster R-CNN architecture at different levels and at two phases of the network. The resulting proposed adaptive detector has the advantage of covering different aspects of the domain shift in order to improve the overall performance. The proposed detector is evaluated on KAIST multispectral dataset and the obtained results demonstrate its effectiveness by improving the adaptability in the thermal domain. Also, by means of comparisons to other existing works, better results are obtained. Additional experiments are conducted on other datasets to further justify the obtained results.

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum N c = 100~10000 and the signal to noise ratio SNR = 0.05–1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the N c >1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of 235U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

The study of refractive index of liquids becoming increasingly popular in diverse areas to monitor chemical, pharmaceutical, food, scientific, biochemical, medical, etc. processes and procedures. In the present paper a low cost, simple, compact, reliable, robust, miniaturized and sturdy Refractive Index sensor is reported. It is configured by using a set of two multimode PCS fiber of 50 cm length in each, connected one to the source of wavelength 660 nm and other to a light detector. A glass rod drawn in the form of ‘U’, of compatible dimensions to that of two multimode fibers, was connected between source and detector by jointing to the remaining ends of the fibers, thus creating a sensing zone. Maintaining the binary liquids of Toluene and t-Butanol at the sensing zone at various temperatures, the light launched from source was monitored in the detector at the detector end. By forming relationship between index of refraction and power output at the temperature range of 10 °C to 60 °C, the sensor expected to be used to detect the index of refraction of liquids with maximum resolution of the order of 10-5 in the operating range from 1.36312nD to 1.50915nD.

This paper introduces a novel concept of the pulsed neutron facility (PNF) for maximizing the production of the thermal neutrons and its application to medical use based on prompt gamma neutron activation analysis (PGNAA) using Monte Carlo simulations. The PNF consists of a compact D-T neutron generator, a graphite pile, and a detection system using Cadmium telluride (CdTe) detector arrays. The configuration of fuel pins in the graphite monolith and the design and materials for the moderating layer were studied to optimize the thermal neutron yields. Biological samples – normal and cancerous breast tissues – including chlorine, a trace element, were used to investigate the sensitivity of the characteristic γ-rays by neutron-trace material interactions and the detector responses of multiple particles. Around 90 % of neutrons emitted from a deuterium-tritium (D-T) neutron generator thermalized as they passed through the graphite stockpile. The thermal neutrons captured the chlorines in the samples, then the characteristic γ-rays with specific energy levels of 6.12, 7.80 and 8.58 MeV were emitted. Since the concentration of chlorine in the cancerous tissue is twice that in the normal tissue, the count ratio of the characteristic γ-rays of the cancerous tissue over the normal tissue is approximately 2.

A radiation hardness study of semi-insulating GaAs detectors against 5 MeV electrons is presented in this paper. The influence of cumulative dose in the range from 136 up to 2000 kGy on alpha-spectrometry of 241Am is evaluated for investigated detectors. The characterising parameter, the charge collection efficiency, was negatively affected by cumulative dose applied. At 200 V detector reverse bias it rapidly decreased from initial 45% it to 15% at 600 kGy proceeding with mild deterioration down to 6% at 2000 kGy. The operability of detector was well preserved through whole range of doses. The alpha- and gamma-spectrometry of the same radioactive source are compared.

With the recent progress in electronic skin, wearable medical devices and robotic systems, efforts have been devoted to capacitive proximity sensor and organic semiconductor proximity sensor. The former shows the proximity perception ability towards the conductor, and the latter enables proximity detection of the electrically charged objects. Here, by embedding rubrene organic semiconductor proximity sensor (insulator detector) and a parallel plate capacitive proximity sensor (metal detector) on the polydimethylsiloxane (PDMS) elastic substrate, we merge two-type proximity sensors in a single platform to realize a flexible, conformal composite proximity sensor, which enables good adherence onto the curved objects and detection of fingers, metal, and insulators, showing the strong application potential in wearable electronics.

Currently, the top-performing face detectors use Convolution Neural Networks (CNN). These detectors have high performance but require large computational resources. Hence, it is a challenge to use these for practical applications with limited resources. To address this problem, this research paper proposes a single-stage face detector for practical applications with better accuracy and speed. The proposed face detector is a lightweight and fast CNN-based face detector. To achieve fast detection speed, by swiftly reducing the size of the input feature space through a carefully designed architecture using depth-wise convolution and Receptive Field Blocks (RBF). The high accuracy of the method is achieved by preserving the feature information from the early layers using CReLU (Concatenate Rectified Linear Units) activation in the CNN architecture. To detect faces of different pixel sizes, we used parallel RF blocks, which use multiple filters of different kernel sizes and dilation. The proposed method was tested on benchmark datasets such as WIDER FACE, AFW, and PASCAL faces, and performed well when compared to recent state-of-the-art real-time face detection algorithms.

This manuscript focuses on the basic idea of vehicle detection techniques used for its detection. Here discussed the common idea for vehicle detection techniques that are used for different scenario. Traditional and modern methods that are used for vehicle detection method are explained. Statistic method is one of the traditional methods that are used for tracking of vehicles. Blob detection and its analysis is one of the processes in detecting vehicles. You only look once-v3 (YOLO-v3) is a detection method base on the idea of single shot detector (SSD) which is fully optimized and enhanced the exposure capability of small scale target objects. The results show that all the techniques can be used for detection but modern technique based on deep learning is more optimized and accurate. It is very necessary that great attention be paid to image and video recognition be paid to the efficiency of the device. It is difficult to locate and distinguish when flows are high. Using the principle of spatial consolidation, it then updates the network structure to enable real-time detection and statistical flow recalculation.

While volume-counting detectors are currently installed in many freeways and have the capabilities of collecting high-resolution traffic information, travel time estimations are usually only based on spot speeds collected by the detectors, which could only produce rough estimates of travel time during peak hours when roadways are congested. In addition to spot speeds, these detectors provide directional volume counts and occupancy, which are useful for a better travel time estimation. This research used data from volume counting detectors and made Adaptive Neural Fuzzy Inference System (ANFIS) to automatically estimate real-time travel times. ANFIS’s ability to learn traffic patterns allows accurate and reliable real-time travel time estimation even with missing or corrupted data. ANFIS also proves to be a powerful tool for estimating future travel times on freeways. For easier use by practitioners and researchers, the method used in this study was implemented into a software package, named FTTE (Freeway Travel Time Estimator). Finally, a case study was conducted using the new approach and the results were compared for both congested and uncongested traffic conditions.

The High Energy Modular Ensemble of Satellites (HERMES) project intends to build an all-sky monitor operating from keV to MeV for the detection and localisation of transient events, like gamma ray bursts. HERMES is a modular observatory composed by detectors on-board of nanosatellites. HERMES aims to revolutionise the world of multi-messenger astrophysics thanks to the innovative concept of a modular instrument based on small satellites and characterised by reduced design and development times and low costs, in the face of high technological content and scientific profile of the mission. A first part of the project, HERMES Technological Pathfinder, composed of three CubeSats, aims to demonstrate the feasibility of detecting transient phenomena in high energy with small satellites. The next phase of the project, HERMES Scientific Pathfinder, will expand the constellation up to six satellites, allowing routinely accurate triangulation measurements. This paper presents an analysis that aims to be propaedeutic for the design of the final HERMES Full Constellation, which will be an all-sky monitor made up of tens/hundreds of nanosatellites in Low Earth Orbit with a total effective area of ∼ m 2 . An adequate number of nanosatellites, simultaneously detecting a transient, provides a source localisation accuracy of the order of magnitude of few arcmin and large effective area. The objective of this work is to define possible strategies of injection in orbit of the complete constellation. In this first analysis we used only the number of triangulable Gamma Ray Bursts (simultaneously detected by at least three nanosatellites) as discriminating factor in order to maximise the constellation performance. By achieving the goal of at least a mean number of 20 Gamma Ray Bursts triangulated per nanosatellite over the two years life-time, optimal configurations for the complete constellation, consisting of seven to fourteen CubeSats, have been identified.

In this study, the performance of sequence tracking methods for multiple interaction events in specific CdZnTe Compton imagers was evaluated using Monte Carlo simulations. The Compton imager consisted of a 6 × 6 array of virtual Frisch-grid CZT crystals, where the dimensions of each crystal were 5 × 5 × 12 mm3. The sequence tracking methods for another Compton imager that consists of two identical CZT crystals arrays were also evaluated. When 662 keV radiation was incident on the detectors, the percentages of the correct sequences determined by the simple comparison and deterministic methods for two sequential interactions were identical (∼80 %), while those evaluated using the minimum squared difference method (55–59 %) and Three Compton method (45–55 %) for three sequential interactions, differed from each other. The reconstructed images of a 662 keV point source detected using single and double arrays were evaluated based on their angular resolution and signal-to-noise ratio, and the results showed that the double arrays outperformed single arrays.

Conventional semi-active laser guidance takes advantage of the laser designator to illuminate the stable and uniform laser spot on target precisely. The seeker collects the reflected light by a quadrant detector and outputs the relative position information to guide the missile to the illuminating laser spot. However, the designation and guidance accuracy could be jeopardized by the randomly drifting of laser spot caused by the instability of designation platform and air turbulence. In this work, ghost imaging technique is adapted to a quadrant detector semi-active seeker by utilizing structured illumination on the target. With a series of structured illumination masks, the signals from the quadrant detector are multiplexed to perform calculation of the target relative position as well as image reconstruction of the illuminated area simultaneously. Automatic target recognition methods could be further applied to the reconstructed image to calculate the designating error and correct the guidance. The results of simulation and experiment demonstrate that the proposed method could improve the guidance accuracy in many circumstances which would lead to attacking deviation if conventional semi-active laser guidance is used.

Synthetic aperture radar (SAR) is playing an important role in maritime domain awareness. As a fundamental ocean mission, SAR ship detection can offer high-quality services for marine surveillance. It receives widespread concern in the SAR remote sensing community. Recently, deep learning (DL) has greatly improved SAR ship detection accuracy. However, there are still some unperceived imbalance problems that seriously hinder further accuracy improvements among current DL-based SAR ship detectors. Therefore, we propose a novel concept of balance learning (BL) for high-quality SAR ship detection. We first point out the four unperceived imbalance problems, i.e., image sample scene imbalance, positive negative sample imbalance, ship scale feature imbalance, and classification regression task imbalance. Then, we offer some profound insights into these imbalances. Immediately, we propose four effective solutions to handle the above four imbalances correspondingly, i.e., balance scene learning mechanism (BSLM), balance interval sampling mechanism (BISM), balance feature pyramid network (BFPN), and balance classification regression network (BCRN). Finally, combined with the four solutions, a novel balance learning network (BL-Net) is proposed. Ablation studies can confirm each solution’s effectiveness. Experimental results on five open datasets (SSDD, Gaofen-SSDD, Sentinel-SSDD, SAR-Ship-Dataset, and HRSID) reveal BL-Net’s state-of-the-art SAR ship detection performance compared to the other 30 DL-based SAR ship detectors. Specifically, in contrast to the current most competitive method, the accuracy increase of BL-Net is 2.98% on SSDD, 1.97% on Gaofen-SSDD, 1.49% on Sentinel-SSDD, 0.55% on SAR-Ship-Dataset, and 4.95% on HRSID. Last but not least, the satisfactory ship detection results on another two large-scene Sentinal-1 SAR images confirm BL-Net’s strong migration capability. This indicates BL-Net’s potential value in marine surveillance.

In 2009, the King Abdulaziz City for Science and Technology (KACST) launched a project involving the establishment of the first radiation detector laboratory in Saudi Arabia. This project aims to d...

This paper is concerned with the design of false data injection attacks (FDIAs) against fault detectors (FD) for linear cyber-physical systems (CPSs). The purpose of the attacker is to design an stealthy attack scheme such that the defenders cannot detect the faults in time or fail to detect the faults, and at the same time the robustness of the CPS is deteriorated. Unlike the existing optimal attack strategies (OASs) that are established based on sufficient conditions, necessary and sufficient conditions (NASCs) are established to maximize the degradation of the FD performance and robustness of CPS while maintaining stealth. Subsequently, an OAS is constructed by solving coupled backward recursive Riccati difference equations (RDEs). Finally, two simulation examples are employed to show the effectiveness of the designed attack scheme.

Based on the CORSIKA simulations of inclined extensive air sowers with proton and iron as primary particles, we have calculated the rise time ( t 1 / 2 ) of detected pulses in particle detectors. We performed the simulations for six different zenith angles between 20° and 53°. It is shown that the rise time has a strong dependence on the core location, zenith, and azimuth angle of a primary particle. Moreover, azimuthal asymmetries of the rise time, which is a characteristic of inclined extensive air showers, are investigated. The potential use of this parameter for mass discrimination of cosmic rays is also discussed.

Online multi-object tracking is a process of extending multi-object trajectories with only past information. In this process, tracking drift, missing detection, and occlusion among objects are common problems. To address the problem of tracking drift, in this paper, a detector–tracker integration framework is proposed and the framework includes the linear regression model (LRM) that can detect and track objects simultaneously. The two kinds of results are combined using LRM to suppress tracking drift. Moreover, to overcoming missing detection and the occlusion, a structural similarity calculation based on the attention mechanism is proposed. The attention mechanism is utilized to learn the discriminative features of each object, and a structural similarity calculation are used to improve the ability to extend the objects’ trajectories. Based on these, we design a multi-object tracking strategy, enabling the trajectory’s initialization, extension, and termination. Finally, experiments and analysis are executed on MOT16 and MOT17 benchmarks datasets, and the proposed method obtains multi-object tracking accuracy of 60%.

Although the individual channel readout method can improve the performance of PET detectors with pixelated photo-sensors, such as silicon photomultiplier (SiPM), this method leads to a significant increase in the number of readout channels. In this study, we proposed a novel multiplexing method that could effectively reduce the number of readout channels to reduce system complexity and development cost. The proposed multiplexing circuit was designed to generate bipolar pulses with different zero-crossing points by adjusting the time constant of the high-pass filter connected to each channel of a pixelated photo-sensor. The channel position of the detected gamma-ray was identified by estimating the width between the rising edge and the zero-crossing point of the bipolar pulse. In order to evaluate the performance of the proposed multiplexing circuit, four detector blocks, each consisting of a 4 × 4 array of 3 mm × 3 mm × 20 mm LYSO and a 4 × 4 SiPM array, were constructed. The average energy resolution was 13.2 ± 1.1% for all 64 crystal pixels and each pixel position was accurately identified. A coincidence timing resolution was 580 ± 12 ps. The experimental results indicated that the novel multiplexing method proposed in this study is able to effectively reduce the number of readout channels while maintaining accurate position identification with good energy and timing performance. In addition, it could be useful for the development of PET systems consisting of a large number of pixelated detectors.

The cross sections for the natRe(γ,xn) reactions at the bremsstrahlung end-point energies of 30 MeV and 40 MeV have been measured by the activation and the off-line γ-ray spectrometric techniques using a High Purity Germanium (HPGe) detector. The measured natRe(γ,xn) reaction cross sections were compared to the theoretically calculated cross sections using TALYS 1.9 and EMPIRE 3.2 computer codes. The measurements allowed the determination of the Isomeric Cross section Ratios (ICR) for the 182m,gRe and 184m,gRe isomeric pairs. In addition, we have determined a semi-empirical ICR value for the 186m,gRe isomeric pair. These results for the bremsstrahlung end-point energies of 30 MeV and 40 MeV are obtained for the first time.