Basic Detector Research Articles

Performance Analysis of Avalanche Photodiode for Direct Detection LADAR Receivers (Dept.E)

LADAR systems constitute a direct extension of the conventional radar techniques. Because they operate at much shorter wavelengths, LADARs have the unique capability to generate 3D images of objects. These laser systems have many applications in the defense gelds concerning target detection and identification. The extraction of these features depends on the processing algorithms, target properties and 3D images quality. Our scope in this research is to use the Avalanche photodiode as a basic detector in three dimensional imaging LADAR systems and analyze its behavior against different operating conditions. Avalanche photodiode in its linear mode gives good performance only for high count rate of photo-electrons. In the case of low count rates (extremely weak signals), this device must be biased above its breakdown voltage in order to have higher sensitivity. This situation is known in the literature as Geiger-mode operation. This mode of operation suffers from mean primary noise rates in the measured interval. This in turn may cause the detector not respond before receiving the desired signal. When the Avalanche photodiode is biased below its breakdown voltage with highest achievable gain value, and followed by an ultra low noise amplifier, it becomes sensitive to the single photo-electron. This operating mode is called Linear-mode single photon. The operation of the detector in this mode overcomes the drawbacks of the Geiger-mode. Detection and false alarm probabilities are analyzed for each one of these operating situations.

Development and Validation of RP-HPLC Method using Photodiode Array or Diode-Array Detection Detector for simultaneous Estimation of the Amlodipine Besylate and Lisinopril in Fixed-Dose Formulation

A basic and specific HPLC-PDA (high-performance liquid chromatography-photodiode array) detector showing approach was created for the simultaneous estimation of anti-hypertensive medications—amlodipine besylate (AMD) and lisinopril (LIS). A successful chromatography method was developed using the SunFire C8 column (4.6 × 150 mm, 5 μm) with gradient elution of the mobile phase composed of 85:25 potassium dihydrogen phosphate in water and methanol at a flow rate of 0.7 mL/min. The wavelength was set at 212 nm for the simultaneous estimation of AMD and LIS. The retention time obtained was 5.1 and 10.5 minutes for AMD and LIS, respectively. Descriptive performance of the proposed HPLC methodology was measurably approved as for framework accuracy, linearity, robustness, specificity, precision, and forced degradation for identification and evaluation limits. The linearity range for AMD and LIS correlation coefficient value obtained was R2 andgt; 0.999. The drugs were exposed to forced degradation conditions—acidic, alkali, oxidation, etc. The newly developed reverse phase high performance liquid chromatography (RP-HPLC) method was applied for the detection of the referred to anti-hypertensive medications in their combination pharmaceutical tablets.

An Algorithm for Detecting Coherence in Cyclone Global Navigation Satellite System Mission Level-1 Delay-Doppler Maps

An algorithm for detecting coherence in Cyclone Global Navigation Satellite System (CYGNSS) mission delay-Doppler maps (DDMs) is presented. Because CYGNSS DDMs report only the observed power without phase information, the algorithm uses estimates of power “spread” within the DDM to flag coherency. Since the estimate used is a ratio of the powers in differing portions of the DDM, it is less sensitive to absolute power calibration and to the GPS C/A code type observed, and is applied to CYGNSS Level-1 uncalibrated DDMs. The basic detector formulation is described along with modifications to improve performance in lower signal-to-noise ratio (SNR) situations. The required detection thresholds are determined using matchups with CYGNSS “Raw I/F” mode measurements for which the DDM phase can be computed and used to identify coherence more precisely. Application of the final detector over a large CYGNSS data set suggests that approximately 8.9% of all inland returns are coherent. Inland regions persistently identified as coherent were found largely to be associated with the presence of water bodies. A smaller set of desert locations apparently having very low surface roughness were also found to be associated with persistent coherence. The detector was also applied to a set of ocean measurements, with the results showing that persistent coherence is limited to areas with sheltered waters. Ocean tests avoiding such regions indicate that the detector's false-alarm rate is approximately 0.0012% for the detection threshold used.

CR-FPN: channel relation feature pyramid network for object detection

Object detection is an important computer vision task, which aims to locate each object and classify them correctly. Since Convolution Neural Network (CNN) high-level feature map contains more semantic information and low-level feature map contains detail information which helps to locate precisely, fusing high-level feature maps with low-level feature maps is proven to be essential for multi-scale object detection. Recent work usually direct combine feature maps from different levels of CNN. However, as each feature map has different extents of discrimination, combining them directly may turn some valuable information into noise and further degrade performance. To address this issue, we focus on the channel relationship from different level of feature maps and propose a novel network, named Channel Relation Feature Pyramid Network (CR-FPN), that captures long-range relation of channel from different level of feature maps by similarity measure function and further to magnify the most relevant channels and suppress the irrelevant channels. Extensive experiments conducted on MS COCO and Pascal VOC datasets demonstrate the effectiveness of our CR-FPN also with competitive performance. Noting that our CR-FPN can apply to several typical state-of-the-art detectors. Using CR-FPN in a basic Cascade R-CNN detector, our method achieves state-of-the-art single model results on the COCO dataset.

A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning

Automatically detecting mud in bauxite ores is important and valuable, with which we can improve productivity and reduce pollution. However, distinguishing mud and ores in a real scene is challenging for their similarity in shape, color, and texture. Moreover, training a deep learning model needs a large amount of exactly labeled samples, which is expensive and time consuming. Aiming at the challenging problem, this paper proposed a novel weakly supervised method based on deep active learning (AL), named YOLO-AL. The method uses the YOLO-v3 model as the basic detector, which is initialized with the pretrained weights on the MS COCO dataset. Then, an AL framework-embedded YOLO-v3 model is constructed. In the AL process, it iteratively fine-tunes the last few layers of the YOLO-v3 model with the most valuable samples, which is selected by a Less Confident (LC) strategy. Experimental results show that the proposed method can effectively detect mud in ores. More importantly, the proposed method can obviously reduce the labeled samples without decreasing the detection accuracy.

Obtaining diffraction patterns from annular dark-field STEM-in-SEM images: Towards a better understanding of image contrast

This contribution demonstrates experimentally how a series of annular dark-field transmission images collected in a scanning electron microscope (SEM) with a basic solid-state detector can be used to quantify electron scattering distributions (i.e., diffraction patterns). The technique is demonstrated at different primary electron energies with a polycrystalline aluminum sample and two amorphous samples comprising vastly different mass-thicknesses. Contrast reversal is demonstrated in both amorphous samples, suggesting that intuitive image contrast interpretation is not always straightforward even for ultrathin, low atomic number samples. We briefly address how the scattering distributions obtained here can be used as an aid to interpret contrast in annular dark-field images, and how to set up imaging conditions to obtain intuitively interpretable contrast from samples with regions of significantly different thickness.

LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction

A globally consistent map is the basis of indoor robot localization and navigation. However, map built by Rao-Blackwellized Particle Filter (RBPF) doesn’t have high global consistency which is not suitable for long-term application in large scene. To address the problem, we present an improved RBPF Lidar SLAM system with loop detection and correction named LCPF. The efficiency and accuracy of loop detection depend on the segmentation of submaps. Instead of dividing the submap at fixed number of laser scan like existing method, Dynamic Submap Segmentation is proposed in LCPF. This segmentation algorithm decreases the error inside the submap by splitting the submap where there is high scan match error and later rectifies the error by an improved pose graph optimization between submaps. In order to segment the submap at appropriate point, when to create a new submap is determined by both the accumulation of scan match error and the particle distribution. Furthermore, LCPF uses branch and bound algorithm as basic detector for loop detection and multiple criteria to judge the reliability of a loop. In the criteria, a novel parameter called usable ratio was proposed to measure the useful information that a laser scan containing. Finally, comparisons to existing 2D-Lidar mapping algorithm are performed with a series of open dataset simulations and real robot experiments to demonstrate the effectiveness of LCPF.

The development of in-ring reaction measurements at the HIRFL-CSR

Abstract The present paper describes the development of in-ring reaction measurements at the Cooler-Storage Ring of the Heavy Ion Research Facility in Lanzhou (HIRFL-CSR). A Silicon (Si) detector has been developed with cost-efficient methods to meet the requirements of Ultra-High Vacuum (UHV) conditions in a storage ring. The UHV-Si detector and luminosity monitor Si(Li) detector were installed at the experimental cooler storage ring (CSRe). The pilot in-ring reaction experiment about proton elastic scattering on 58Ni 19 + ions has been operated successfully with the basic detector system in inverse kinematics , which opens an opportunity for in-ring reaction measurements at the HIRFL-CSR. This is an important step towards realizing in-ring reaction measurements with large Si detector arrays at the next generation High Intensity heavy ion Accelerator Facility (HIAF).

Gated bidirectional feature pyramid network for accurate one-shot detection

Despite recent advances in machine learning, it is still challenging to realize real-time and accurate detection in images. The recently proposed StairNet detector (Sanghyun et al. in Proceedings of winter conference on applications of computer vision (WACV), 2018), one of the strongest one-stage detectors, tackles this issue by using a SSD in conjunction with a top-down enrichment module. However, the StairNet approach misses the finer localization information which can be obtained from the lower layer and lacks a feature selection mechanism, which can lead to suboptimal features during the merging step. In this paper, we propose what is termed the gated bidirectional feature pyramid network (GBFPN), a simple and effective architecture that provides a significant improvement over the baseline model, StairNet. The overall network is composed of three parts: a bottom-up pathway, a top-down pathway, and a gating module. Given the multi-scale feature pyramid of deep convolutional network, two separate pathways introduce both finer localization cues and high-level semantics. In each pathway, the gating module dynamically re-weights the features before the combining step, transmitting only the informative features. Placing GBFPN on top of a basic one-stage detector SSD, our method shows state-of-the-art results.

Recoil-proton track imaging as a new way for neutron spectrometry measurements

Recoil-proton track imaging (RPTI) is an attractive technique to optically record the tracks of recoil protons in scintillation gas by using realtime imaging devices. For the first time, its use as an online nuclear track detector for neutron spectrometry measurements (NSM) is explored. Based on the RPTI methodology for NSM, a very basic detector system is designed, consisting of the neutron-to-proton recoil system and proton track imaging system. Satisfactory performance of the RPTI neutron spectrometer has been examined with a series of Monte Carlo simulations. Moreover, using well-defined line-proton sources from a tandem accelerator, the capability of the detector for imaging proton tracks at the single-particle level in real time has been validated in preliminary experiments. From the clear single proton tracks in the images, the proton ranges were easily distinguished, and precise proton energy spectra were unfolded, laying a solid experimental foundation for the future implementation of NSM.

Top-tagging at the energy frontier

At proposed future hadron colliders and in the coming years at the LHC, top quarks will be produced at genuinely multi-TeV energies. Top-tagging at such high energies forces us to confront several new issues in terms of detector capabilities and jet physics. Here, we explore these issues in the context of some simple JHU/CMS-type declustering algorithms and the N-subjettiness jet-shape variable tau_32. We first highlight the complementarity between the two tagging approaches at particle-level with respect to discriminating top-jets against gluons and quarks, using multivariate optimization scans. We then introduce a basic fast detector simulation, including electromagnetic calorimeter showering patterns determined from GEANT. We consider a number of tricks for processing the fast detector output back to an approximate particle-level picture. Re-optimizing the tagger parameters, we demonstrate that the inevitable losses in discrimination power at very high energies can typically be ameliorated. For example, percent-scale mistag rates might be maintained even in extreme cases where an entire top decay would sit inside of one hadronic calorimeter cell and tracking information is completely absent. We then study three novel physics effects that will come up in the multi-TeV energy regime: gluon radiation off of boosted top quarks, mistags originating from g -> tt, and mistags originating from q -> (W/Z)q collinear electroweak splittings with subsequent hadronic decays. The first effect, while nominally a nuisance, can actually be harnessed to slightly improve discrimination against gluons. The second effect can lead to effective O(1) enhancements of gluon mistag rates for tight working points. And the third effect, while conceptually interesting, we show to be of highly subleading importance at all energies.

Detection filter design for homogeneous multi-agent networks

A class of large-scale systems with decentralized information structures such as multi-agent systems can be represented by a linear system with a generalized frequency variable. In these models agents are modelled through a strictly proper SISO state space model while the supervisory structure, representing the information exchange among the agents, is represented via a linear state-space model. In this paper the fundamental problem of residual generation, as a basic detector filter design task, is solved in the context of homogeneous multi-agent networks. In order to reduce the dependence of the filter on the particular agent and to exploit the inherent informational structure of the network system the same local-global structure is imposed to the filter as of the original system. It is shown that a stable detection filter can be designed if the same structural conditions are fulfilled as in the unstructured LTI case.

Autonomous Recognition System for Barcode Detection in Complex Scenes

Linear barcode technology has been widely used in our common life, such as in logistics, retailed products and many other applications. Many researches and smart applications focus on how to decode the barcode so that it is difficult to locate precisely when the background becomes very complex. Moreover, many smart apps need human interaction to make sure the detected region is in a correct position of the screen. This paper presents an effective approach to locate the barcodes in real-time without manual disturbing. Basic morphological operations and Parallel Line Segment Detector (P-LSD) are applied to achieve the legal block of barcodes. Our method has been evaluated by a standard database and the experimental results show that our approach is more robuster than other earlier methods.

Synchrotron beam test of a photon counting pixel prototype based on Double-SOI technology

The overall noise performances and first synchrotron beam measurement results of CPIXETEG3b, the first counting type Silicon-On-Insulator (SOI) pixel sensor prototype without crosstalk issue, are reported. The prototype includes a 64 × 64 pixel matrix with 50 μm pitch size. Each pixel consists of an N-in-P charge collection diode, a charge sensitive preamplifier, a shaper, a discriminator with thresholds adjustable by an in-pixel 4-bit DAC, and a 6-bit counter. The study was performed using the beam line 14A at KEK Photon Factory (KEK-PF) . The homogeneous response of the prototype, including charging-sharing effects between pixels were studied. 16 keV and 8 keV monochromatic small size (∼ 10 μm diameter) X-ray beams were used for the charge sharing study, and a flat-field was added for homogenous response investigation. The overall detector homogeneity and the influence of basic detector parameters on charge sharing between pixels has been investigated.

Improved matrix CFAR detector based on K-L divergence and divergence mean}{Improved matrix CFAR detector based on K-L divergence and divergence mean

The target detection of traditional radar, which is usually realized by fast Fourier transforms (FFT) associated with a constant false alarm rate detector, faces the following problems: (1) The Doppler resolution of the FFT is low when the measurement length is short, resulting in Doppler bestriding loss. (2) The broadening of the spectrum causes the clutter energy to disperse toward the target. (3)To the target extended in frequency domain, the single channel detection will lose much energy of the target measures. An improved matrix CFAR detector is proposed, which uses the Kullback-Leibler divergence and divergence mean to replace the geodesic distance and Riemannian mean to overcome the three above-mentioned problems. Its CFAR performance and detection are better than that of the basic matrix CFAR detector; the calculated amount is simultaneously reduced. Finally, the performance of the improved matrix CFAR detector is analyzed by simulation experiments. The results indicate a good performance.

Radiation monitoring of the GEM muon detectors at CMS

The higher energy and luminosity of future High Luminosity (HL) LHC, determines a significant increasing of the radiation background around the CMS subdetectors, and especially in the higher pseudorapidity region. Under such heavy conditions, the RPC (used in muon trigger) most probably could not operate effectively. GEM (Gas Electron Multiplier) detectors have been identified as a suitable technology to operate in the high radiation environment in that region and test at CMS will start in 2016. A monitoring system to control the absorbed radiation dose by the GEM under test is developed. Two types of sensors are used in it: RadFETs for total absorbed dose and p-i-n diodes for particle (proton and neutron) detection. The basic detector unit, called RADMON, contains two sensors of each type and can be installed at each GEM detector. The system has a modular structure, permitting to increase easily the number of controlled RADMONs: one module controls up to 12 RADMONs, organized in three group of four and communicates outside by RS 485 and CANBUS interfaces.

FIELD CALIBRATION OF A TLD ALBEDO DOSEMETER IN THE HIGH-ENERGY NEUTRON FIELD OF CERF.

The new albedo dosemeter-type AWST-TL-GD 04 has been calibrated in the CERF neutron field (Cern-EU high-energy Reference Field). This type of albedo dosemeter is based on thermoluminescent detectors (TLDs) and used by the individual monitoring service of the Helmholtz Zentrum München (AWST) since 2015 for monitoring persons, who are exposed occupationally against photon and neutron radiation. The motivation for this experiment was to gain a field specific neutron correction factor Nn for workplaces at high-energy particle accelerators. Nn is a dimensionless factor relative to a basic detector calibration with 137Cs and is used to calculate the personal neutron dose in terms of Hp(10) from the neutron albedo signal. The results show that the sensitivity of the albedo dosemeter for this specific neutron field is not significantly lower as for fast neutrons of a radionuclide source like 252Cf. The neutron correction factor varies between 0.73 and 1.16 with a midrange value of 0.94. The albedo dosemeter is therefore appropriate to monitor persons, which are exposed at high-energy particle accelerators.

Infrared radiometric measurements of lunar disk temperatures during lunar eclipse on 15th June 2011

Radiometric measurements of the total lunar eclipse on 15th June 2011 were carried out at the KACST observatory (lat. 21.25 N; long. 49.30 E), Jeddah, Saudi Arabia, using a locally designed, constructed and calibrated infrared detector. The basic detector is a Heimann TPS 534 thermopile with a 3° field of view and operating at wavelengths between 8μm and 14μm. The total phase of this eclipse lasted about 100min, making it one of the darkest eclipses this century. The lunar temperature curve of this eclipse was obtained and showed comparable behavior with previously established infrared observations. We found that the lunar surface temperature decreased by about 147K and 220K during the partial and total eclipse phases, respectively, in comparison with the lunar temperature before the eclipse.

The SNO+ Project

The SNO+ project will make use of the basic SNO detector infrastructure to create a large liquid scintillator experiment to explore a variety of fundamental physics, with a strong focus on neutrinoless double beta decay using 130Te. Substantial modifications to the original SNO detector are being effected in order to achieve this. The initial water fill is currently under-way, with the transition to liquid scintillator scheduled for next year. Details of the detector preparation, commissioning and design goals will be presented.

A new generation of detectors for scanning x-ray beam imaging systems

Scanning x-ray beam imaging systems were first developed by American Science and Engineering, Inc. (AS&E) in the early 1970s [1]. Since then, these systems have found a wide range of applications in security inspection and non-destructive testing. Large-area detectors are most frequently used to collect backscattered radiation but smaller transmission detectors are also employed for selected applications. Until recently, only two basic detector designs have been used: large scintillator blocks with attached photomultiplier tubes (PMTs) or large-volume light-sealed boxes, lined with scintillating screens and port windows for PMTs. In both cases, the detectors have required considerable depth to provide acceptable light collection efficiency. A new design recently developed by AS&E relies on wavelength shifting fibres (WSF) for light collection. For the first time, this approach enables the construction of thin large-area detectors. Stacking layers of WSF ribbons and scintillating screens in varying combinations enables optimization of the detection efficiency for different applications. Taking separate readings from different layers provides an energy-sensitive signal combination. Energy sensitivity can be improved further by adding filtration between the signal channels. Several prototype configurations have been built and characterized for both backscatter and transmission imaging. A WSF-based detector has been commercialized for a transmission x-ray imaging application.