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Conference on Infrared Technology and Applications XXXVIII

作者： Gawron, Waldemar Bielecki, Zbigniew Wojtas, Jacek Stanaszek, Dariusz Lach, Jerzy Fimiarz, Maciej Mil Univ Technol 2 Kaliskiego Str PL-00908 Warsaw Poland VIGO Syst SA PL-05850 Ozarow Mazowiecki Poland

ISBN: (纸本)9780819490315

The paper presents the new infrared detection module developed at the VIGO System Ltd. Its high sensitivity of was achieved by both matching the IR detector to the preamp and minimizing noises. High sensitivity of the detector was achieved by using photodiodes with immersion lens. Immersion lens enables optimization of the detector area, decreasing detector capacity and time constant. Detector noise was reduced as a result of photodiode cooling by means of a thermoelectric cooler and reverse biasing. Developed module is dedicated to NOx optoelectronic sensors operates basing on Cavity Enhanced Absorption Spectroscopy technique.

关键词： Uncooled photodetectors HgCdTe detection module optoelectronic sensors

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APD-based detection module of Single-photons in Chemiluminescence Immunoassay System

APD-based Detection Module of Single-photons in Chemilumines...

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2nd International Conference on Mechanical Design and Power Engineering (ICMDPE 2013)

作者： Meng, Hao Guo, Yangkuan Wen, Chao Beijing Informat Sci & Technol Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100192 Peoples R China

ISBN: (纸本)9783038350019

To accurately and rapidly detect single-photons is of the utmost importance for a Chemiluminescence Immunoassay (CLIA) system. An APD-based detection module of single-photons is designed in this paper. By comparison with PMT, APD possesses the advantages of higher detection sensitivity, lower dark current, smaller size, lower power consumption and higher integrity. The designed module can accurately detect single-photons with 22MHz frequency. The experimental results show that the module can be a basis for designing portable CLIA systems.

关键词： Chemiluminescence Immunoassay Single-photons detection module APD

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Terahertz detection module based on antenna-coupled AlGaN/GaN HEMTs

Terahertz detection module based on antenna-coupled AlGaN/Ga...

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Conference on Earth and Space - From Infrared to Terahertz (ESIT)

作者： Zhu, Y. F. Ding, Q. F. Zhang, J. F. Shangguan, Y. Xiang, L. Y. Sun, J. D. Qin, H. Univ Sci & Technol China Sch Nanotech & Nanobion Hefei 230026 Peoples R China Chinese Acad Sci Suzhou Inst Nanotech & Nanobion Key Lab Nanodevices & Applicat Suzhou 215123 Peoples R China Key Lab Nanodevices Jiangsu Prov Suzhou 215123 Peoples R China

ISBN: (纸本)9781510661196;9781510661202

To accommodate variable terahertz application situations, a compact, high-sensitivity and room-temperature terahertz detection module is designed and demonstrated. The detection module with a volume of less than 350 cm(3) integrates a quasi-optically coupled terahertz detector, complementary-metal-oxide-semiconductor-based (CMOS-based) voltage amplifier circuit and bias circuit. An antenna-coupled AlGaN/GaN high-electron-mobility transistor (HEMT) are designed to detect terahertz waves by using self-mixing mechanism. The electrical signal from the detector chip is amplified by a voltage amplifier circuit. The amplifier circuit's voltage gain can be adjusted from 100 to 700 to accommodate different requirements. The bias circuit provides bias voltage to the gate of the detector. Ability to detect both continuous and pulsed terahertz waves by the module is demonstrated. Under a coherent continuous terahertz irradiation from 0.73 to 1.13 THz, an average noise-equivalent power (NEP) of 23.6 pW/root Hz, a maximum optical responsivity of 1281 V/W (w/o Gain) and a minimum NEP of 15.3 pW/root Hz are achieved. Under a 4.3 THz pulsed radiation from quantum cascade laser (QCL), the module has a peak optical responsivity of 26 V/W (with Gain = 700) and a NEP of 567 nW/root Hz. The rise time of the output signal is 1.14 mu s and the fall time is 0.78 mu s when the module is operated at a maximum amplification gain of 700 and 6 kHz modulation frequency. To further enhance the sensitivity of the detection module, the design of the detector and the noise of the circuit need to be considered.

关键词： AlGaN/GaN terahertz detection module quantum cascade laser

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Performance analysis of LYSO-SiPM detection module for X-ray communication during spacecraft reentry blackout

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2021年 1013卷 165673-165673页

作者： Liu, Yunpeng Dang, Peng Tang, Xiaobin Mu, Junxu Feng, Zhaopeng Nanjing Univ Aeronaut & Astronaut Dept Nucl Sci & Technol Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Minist Ind & Informat Technol Key Lab Nucl Technol Applicat & Radiat Protect As Nanjing 210016 Peoples R China

In view of the demand for high-energy and high-bandwidth signal detection in X-ray communication (XCOM) during spacecraft reentry blackout, a detection module using fast-decay LYSO scintillator coupled with Silicon photomultiplier (SiPM) was proposed in this study. The theoretical model based on the Monte Carlo simulation and Personick' theory was established and the detector prototype was built. Moreover, the signal to noise ratio (SNR) and bit error rate (BER) performance were investigated. Results present that the SNR of 19.8 dB, 29.9 dB and 35.5 dB could be reached for X-ray with the energy of 17 keV, 59.5 keV and 122 keV respectively. This energy response characteristic is much suitable for the X-ray signal detection during spacecraft reentry blackout. Additionally, the deviation of SNR corresponding to the BER between the theoretical and experimental results does not exceed 0.3 dB when the data rate is 100 kbps, which proves the validity of the theoretical model. Furthermore, the numerical analysis shows that the scheme demonstrates high SNR and low BER performance in high bandwidth and low signal receiving power, which explains the superiorities in X-ray signal detection of the design. This work provides the theoretical basis and design reference for the LYSO-SiPM X-ray signal detection module.

关键词： Bit error rate detection module LYSO SiPM Signal to noise ratio X-ray communication

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Design and implementation of network attacks detection module

Design and implementation of network attacks detection modul...

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Third International Conference on Cyberspace Technology (CCT 2015)

作者： Jiefu Gan Li Peng Hang Cheng Yueming Lu China Information Security Certification Center Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China 3Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China

Nowadays, network security not only is a technical issue, but also permeates our lives and work, and affects the socioeconomic and national security issues. This paper carried out in-depth research on the detection of network attacks, and introduced the design and implement of a network attack detection module. To imitate the real conditions, we use one computer as an attacker, one computer as a detector, and another computer as a victim. In this experiment, the detector can detect a lot of network attacks, such like ARP attacks, IP address attacks, UDP flooding attacks, TCP SYN flooding attacks, ICMP flooding attacks. Moreover, the detector alerts when it detect attacks.

关键词： Attacks detection detection module Network attacks Packets Filter detector Computers IP address Flood Network

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detection AND IDENTIFICATION OF GRAPE LEAF DISEASES BASED ON IMPROVED YOLOV5

JOURNAL OF THE ASABE

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JOURNAL OF THE ASABE 2025年第2期68卷 257-266页

作者： Dai, Qiwei Fan, Lili Xie, Tianli Wuhan Polytech Univ Wuhan Hubei Peoples R China

Grape leaf diseases all over the world cannot be ignored;it is one of the important causes of agricultural economic damage. In order to control the cost of manual detection of grape diseases and to overcome the impact on detection accuracy due to the large variation of target scales during automatic detection of grape leaves, the feature pyramid method is commonly used to solve this problem, but it may destroy the feature consistency ofgrape leaves at different scales. In addition, in practical applications, the commonly used methods are difficult to improve the detection accuracy of multi-scale grape leaves while ensuring detection. In this paper, we apply the improved YOLOv5 algorithm, which is used to detect the images ofgrapevine powdery mildew, grape mosaic virus disease, and grape downy mildew, as well as the images of healthy grape leaves. The proposed algorithm is based on the improved feature pyramid network SE-FPN, which added Scaling Feature Fusion (SFF) to enhance feature map information and Enhanced Expansion Feature (EEF) to improve multi-scale accuracy prediction. And replacing the original Path Aggregation Network (PANet) detection module with the Multi-channel Denoising Feature Fusion (MDFF) network improves the scale invariance of features and reduces the inference cost. By experimentally comparing the original YOLOv5 network and the YOLOv5 network with other improved methods applied, considering the following metrics: mean Average Precision (mAP), accuracy, recall, and mean target detection loss, mean classification loss, and runtime of the validation set, the results conclude that the improved model proposed in this paper performs the best, which increases the accuracy by 5.96% and the recall by 4.69% compared to the original YOLOv5 model. The algorithm provides good technical support for real-time detection and control ofgrapevine diseases.

关键词： detection module Disease identification Feature pyramid network Grape leaf disease YOLO

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FAFEnet: A fast and accurate model for automatic license plate detection and recognition

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IET IMAGE PROCESSING 2023年第3期17卷 807-818页

作者： Zhou, Xin Cheng, Yao Jiang, Liling Ning, Bo Wang, Yanhao Dalian Maritime Univ Sch Informat Sci & Technol Dalian Peoples R China East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China

Automatic License Plate detection and Recognition (ALPR) is a key problem in intelligent transportation systems with wide applications in traffic monitoring, electronic toll collection (ETC), intelligent parking lots (IPLs), and elsewhere. Although numerous methods have been proposed in the literature, it is still challenging to strike a good balance between the accuracy and efficiency of ALPR. In this paper, a novel end-to-end CNN-based model is proposed, called Fast and Accurate Network with Feature Enhancement (FAFEnet), to jointly detect the license plates and recognize the characters with high accuracy and efficiency. Specifically, the FAFEnet model seamlessly integrates two CNN-based models, namely the detection and recognition modules, into a unified framework to reduce accumulated errors and computational overheads in two separate steps. The detection module is a lightweight model with only seven convolutional layers yet achieves over 99.8% accuracy rates for license plate detection across all datasets. The recognition module utilizes two feature enhancement blocks to compensate and enhance the shallow character features extracted by the detection module. Furthermore, the joint optimization of detection and recognition modules exploits the feature association in two modules, and thus improves the prediction accuracy while reducing the execution time. Finally, extensive experimental results on several real-world datasets demonstrate that FAFEnet outperforms all the competitors in terms of both accuracy and efficiency.

关键词： feature extraction feature enhancement blocks automatic license plate detection and recognition image recognition recognition module Neural nets Computer vision and image processing techniques intelligent parking lots convolutional neural nets Traffic engineering computing object detection Image recognition lightweight model FAFEnet model CNN-based models intelligent transportation systems ALPR CNN-based model detection module shallow character features

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Imprint Molding of a Microfluidic Optical Cell on Thermoplastics with Reduced Surface Roughness for the detection of Copper Ions

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ANALYTICAL SCIENCES 2016年第1期32卷 85-92页

作者： Wu, Jing Lee, Nae Yoon Gachon Univ Dept BioNano Technol 1342 Seongnam Daero Songnam 461701 Gyeonggi Do South Korea

Here, we introduce a simple and facile technique for fabricating microfluidic optical cells by utilizing a micropatterned polymer mold, followed by imprinting on thermoplastic substrates. This process has reduced the surface roughness of the microchannel, making it suitable for microscale optical measurements. The micropatterned polymer mold was fabricated by first micromilling on a poly(methylmethacrylate) (PMMA) substrate, and then transferring the micropattern onto an ultraviolet (UV)-curable optical adhesive. After an anti-adhesion treatment of the polymer mold fabricated using the UV -curable optical adhesive, the polymer mold was used repeatedly for imprinting onto various thermoplastics, such as PMMA, polycarbonate (PC), and poly(ethyleneterephthalate) (PET). The roughness values for the PMMA, PC, and PET microchannels were approximately 11.3, 20.3, and 14.2 nm, respectively, as compared to those obtained by micromilling alone, which were 15.9, 76.8, and 207.5 nm, respectively. Using the imprint-molded thermoplastic optical cell, rhodamine B and copper ions were successfully quantified. The reduced roughness of the microchannel surface resulted in improved sensitivity and reduced noise, paving the way for integration of the detection module so as to realize totally integrated microdevices.

关键词： Thermoplastics UV-curable optical adhesive imprint molding optical cell roughness reduction detection module

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A method of single-shot target detection with multi-scale feature fusion and feature enhancement

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IET IMAGE PROCESSING 2022年第6期16卷 1752-1763页

作者： Qu, Zhong Shang, Xue Xia, Shu-Fang Yi, Tu-Ming Zhou, Dong-Yang Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol 2 Chongwen Rd Chongqing Peoples R China Southwest Comp Co Ltd Inst Informat Technol Chongqing Peoples R China Chongqing Coll Mobile Commun 1 Vacat Rd Chongqing Peoples R China

The Single Shot MultiBox Detector (SSD) is one of the fastest detection algorithms. Although it has achieved good results in detection, it also has the problem of poor detection effect for small targets and occlusion between objects. Here, the authors propose a new target detection method called single-shot target detection with multi-scale feature fusion and feature enhancement. Here, the authors introduce multi-scale feature fusion module, feature enhancement module and efficient channel attention module, and integrate them into the detection module of the original SSD target detection algorithm to improve the ability of network feature extraction. Experimental results on pascal VOC 2007 datasets show that the proposed algorithm works well when the input size is 300 x 300, the detection speed reaches 41.7 frames per second (FPS) and the detection accuracy reaches 79.6%, which is 2.4% higher than the original SSD target detection algorithm. When the input size is 512 x 512, the detection accuracy is 81.9%, and the detection speed reaches 36.5 FPS, which is 3.2% higher than the original SSD target detection algorithm. According to the experimental results, our algorithm has a better performance when there are many objects in the image and there is occlusion.

关键词： network feature extraction detection speed single-shot target detection object detection detection module image enhancement detection accuracy single shot multibox detector efficient channel attention module feature enhancement multiscale feature fusion module original SSD target detection algorithm feature extraction

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Stream-wise detection of Surreptitious Traffic over DNS 19

Stream-wise Detection of Surreptitious Traffic over DNS

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19th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

作者： Cejka, Tomas Rosa, Zdenek Kubatova, Hana CESNET Ale Zikova 4 Prague 16000 6 Czech Republic Czech Tech Univ FIT Prague 16000 6 Czech Republic

ISBN: (纸本)9781479957255

The Domain Name System (DNS) belongs to crucial services in a computer network. Because of its importance, DNS is usually allowed in security policies. That opens a way to break policies and to transfer data from/to restricted area due to misusage of a DNS infrastructure. This paper is focused on a detection of communication tunnels and other anomalies in a DNS traffic. The proposed detection module is designed to process huge volume of data and to detect anomalies at near real-time. It is based on combination of statistical analysis of several observed features including application layer information. Our aim is a stream-wise processing of huge volume of DNS data from backbone networks. To achieve these objectives with minimal resource consumption, the detection module uses efficient extended data structures. The performance evaluation has shown that the detector is able to process approximately 511 thousand DNS flow records per second. In addition, according to experiments, a tunnel that lasts over 30 seconds can be detected in a minute. During the on-line testing on a real traffic from production network, the module signalized on average over 60 confirmed alerts including DNS tunnels per day.

关键词： Internet computer network security data structures DNS data DNS infrastructure DNS tunnels application layer information backbone networks communication tunnel detection computer network data transfer detection module domain name system extended data structures production network security policies statistical analysis stream-wise detection stream-wise processing surreptitious traffic Data structures Monitoring Probes Protocols Real-time systems Servers Tunneling

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