Detection Performance Analysis of Space-Based Radar to near Space Hypersonic Target

2014 ◽  
Vol 981 ◽  
pp. 730-734
Author(s):  
Song Xiao ◽  
Xian Si Tan ◽  
Hong Wang ◽  
Zhi Fang Zuo
Keyword(s):  
Performance Analysis ◽  
Detection System ◽  
Detection Performance ◽  
Flight Speed ◽  
Flight Distance ◽  
Range Searching ◽  
Detection Range ◽  
Near Space ◽  
Tracking Mode ◽  
Flight Height

Aimed at the problem that it is hard to detect near space hypersonic target for conventional radar because of its high flight speed, flight height, and flight distance and so on, the detection performance of space-based radar to near space hypersonic target was analyzed. The characteristics of near space hypersonic target and the advantages of space-based radar was introducted, and then, the detection performances of space-based radar to near space hypersonic target from detection range, searching and tracking mode were studied. It has certain guiding significance for space-based radar development and perfection of near space detection system.

scholarly journals DNA tetrahedron-mediated immune-sandwich assay for rapid and sensitive detection of PSA through a microfluidic electrochemical detection system

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Dezhi Feng ◽  
Jing Su ◽  
Yi Xu ◽  
Guifang He ◽  
Chenguang Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Reaction Time ◽  
Electrochemical Detection ◽  
Detection System ◽  
Detection Performance ◽  
Pdms Layer ◽  
Microfluidic Chips ◽  
Serum Samples ◽  
Gold Nanoflowers ◽  
Dna Tetrahedron

AbstractProstate-specific antigen (PSA) is the most widely used biomarker for the early diagnosis of prostate cancer. Existing methods for PSA detection are burdened with some limitations and require improvement. Herein, we developed a novel microfluidic–electrochemical (μFEC) detection system for PSA detection. First, we constructed an electrochemical biosensor based on screen-printed electrodes (SPEs) with modification of gold nanoflowers (Au NFs) and DNA tetrahedron structural probes (TSPs), which showed great detection performance. Second, we fabricated microfluidic chips by DNA TSP-Au NF-modified SPEs and a PDMS layer with designed dense meandering microchannels. Finally, the μFEC detection system was achieved based on microfluidic chips integrated with the liquid automatic conveying unit and electrochemical detection platform. The μFEC system we developed acquired great detection performance for PSA detection in PBS solution. For PSA assays in spiked serum samples of the μFEC system, we obtained a linear dynamic range of 1–100 ng/mL with a limit of detection of 0.2 ng/mL and a total reaction time <25 min. Real serum samples of prostate cancer patients presented a strong correlation between the “gold-standard” chemiluminescence assays and the μFEC system. In terms of operation procedure, cost, and reaction time, our method was superior to the current methods for PSA detection and shows great potential for practical clinical application in the future.

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

scholarly journals A Compact Detection Platform Based on Gradient Guided-Mode Resonance for Colorimetric and Fluorescence Liquid Assay Detection

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2797
Author(s):  
Jing-Jhong Gao ◽  
Ching-Wei Chiu ◽  
Kuo-Hsing Wen ◽  
Cheng-Sheng Huang
Keyword(s):  
Limit Of Detection ◽  
Detection System ◽  
Fluorescence Emission ◽  
Assay Sensitivity ◽  
Detection Range ◽  
Current Form ◽  
Guided Mode ◽  
Spectral Detection ◽  
Guided Mode Resonance ◽  
Colorimetric Assays

This paper presents a compact spectral detection system for common fluorescent and colorimetric assays. This system includes a gradient grating period guided-mode resonance (GGP-GMR) filter and charge-coupled device. In its current form, the GGP-GMR filter, which has a size of less than 2.5 mm, can achieve a spectral detection range of 500–700 nm. Through the direct measurement of the fluorescence emission, the proposed system was demonstrated to detect both the peak wavelength and its corresponding intensity. One fluorescent assay (albumin) and two colorimetric assays (albumin and creatinine) were performed to demonstrate the practical application of the proposed system for quantifying common liquid assays. The results of our system exhibited suitable agreement with those of a commercial spectrometer in terms of the assay sensitivity and limit of detection (LOD). With the proposed system, the fluorescent albumin, colorimetric albumin, and colorimetric creatinine assays achieved LODs of 40.99 and 398 and 25.49 mg/L, respectively. For a wide selection of biomolecules in point-of-care applications, the spectral detection range achieved by the GGP-GMR filter can be further extended and the simple and compact optical path configuration can be integrated with a lab-on-a-chip system.

scholarly journals Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar

2021 ◽  
Vol 13 (4) ◽  
pp. 701 ◽  
Author(s):  
Binbin Wang ◽  
Hao Cha ◽  
Zibo Zhou ◽  
Bin Tian
Keyword(s):  
Fourier Transform ◽  
Target Detection ◽  
Time Integration ◽  
Detection Performance ◽  
Bistatic Radar ◽  
Detection Range ◽  
Robust Detection ◽  
Coherent Integration ◽  
Long Time ◽  
Long Time Integration

Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. In this paper, the extensive cancellation algorithm (ECA), which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace, is first applied in GNSS-based bistatic radar. As a result, the clutter has been removed from the surveillance channel effectively. For long time integration, a modified version of the Fourier transform (FT), called long-time integration Fourier transform (LIFT), is proposed to obtain a high coherent processing gain. Relative acceleration (RA) is defined to describe the Doppler variation results from the motion of the target and long integration time. With the estimated RA, the Doppler frequency shift compensation is carried out in the LIFT. This method achieves a better and robust detection performance when comparing with the traditional coherent integration method. The simulation results demonstrate the effectiveness and advantages of the proposed processing method.

scholarly journals Design of a Microwave Power Detection System in the 5G-Communication Frequency Band

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2674
Author(s):  
Qingying Ren ◽  
Wen Zuo ◽  
Jie Xu ◽  
Leisheng Jin ◽  
Wei Li ◽  
...  
Keyword(s):  
Frequency Band ◽  
Microwave Power ◽  
Communication Systems ◽  
Detection System ◽  
Measurement Sensitivity ◽  
Detection Range ◽  
Detection Systems ◽  
Detection Frequency ◽  
Communication Frequency ◽  
5G Communication

At present, the proposed microwave power detection systems cannot provide a high dynamic detection range and measurement sensitivity at the same time. Additionally, the frequency band of these detection systems cannot cover the 5G-communication frequency band. In this work, a novel microwave power detection system is proposed to measure the power of the 5G-communication frequency band. The detection system is composed of a signal receiving module, a power detection module and a data processing module. Experiments show that the detection frequency band of this system ranges from 1.4 GHz to 5.3 GHz, the dynamic measurement range is 70 dB, the minimum detection power is −68 dBm, and the sensitivity is 22.3 mV/dBm. Compared with other detection systems, the performance of this detection system in the 5G-communication frequency band is significantly improved. Therefore, this microwave power detection system has certain reference significance and application value in the microwave signal detection of 5G communication systems.

Optical design of MWIR detection system for near-space infrared detecting

2015 ◽  
Author(s):  
Pei-pei Yan ◽  
Kai Liu ◽  
Gang Li ◽  
Qiusha Shan ◽  
Jing Duan ◽  
...  
Keyword(s):  
Detection System ◽  
Optical Design ◽  
Near Space

scholarly journals Gold-nanourchin complexed silicon dioxide-probe on gap-fingered interdigitated electrode surface for Parkinson’s Disease determination by current–volt measurement

2021 ◽  
Vol 11 ◽  
pp. 184798042098735
Author(s):  
Xiaohong Li ◽  
Wei Shi ◽  
Wenyan Zhang ◽  
Weiyao Chen ◽  
Dan Cao ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Silicon Dioxide ◽  
Limit Of Detection ◽  
Detection System ◽  
Interdigitated Electrode ◽  
Limit Of Quantification ◽  
Neurofilament Light Chain ◽  
Neurofilament Light ◽  
Detection Range

Parkinson’s disease (PD) is a nervous disorder, affects physical movement, and leads to difficulty in balancing, walking, and coordination. A novel sensor is mandatory to determine PD and monitor the progress of the treatment. Neurofilament light chain (NfL) has been recognized as a good biomarker for PD and also helps to distinguish between PD and atypical PD syndromes. Immunosensor was generated by current–volt measurement on gap-fingered interdigitated electrode with silicon dioxide surface to determine NfL level. To enhance the detection, anti-NfL antibody was complexed with gold-nanourchin and immobilized on the sensing electrode. The current–volt response was gradually increased at the linear detection range from 100 fM to 1 nM. Limit of detection and sensitivity were 100 fM with the signal-to-noise ratio at n = 3 on a linear curve ( y = 0.081 x + 1.593; R 2 = 0.9983). Limit of quantification falls at 1 pM and high performance of the sensor was demonstrated by discriminating against other neurogenerative disease markers, in addition, it was reproducible even in serum-spiked samples. This method of detection system aids to measure the level of NfL and leads to determine the condition with PD.

scholarly journals Cost-Sensitive Approach to Improve the HTTP Traffic Detection Performance on Imbalanced Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenmin Li ◽  
Sanqi Sun ◽  
Shuo Zhang ◽  
Hua Zhang ◽  
Yijie Shi
Keyword(s):  
Feature Extraction ◽  
Cost Function ◽  
Network Traffic ◽  
Detection System ◽  
Imbalanced Data ◽  
Detection Performance ◽  
Learning Technology ◽  
The Cost ◽  
Decision Boundaries ◽  
Traffic Detection

Aim. The purpose of this study is how to better detect attack traffic in imbalance datasets. The deep learning technology has played an important role in detecting malicious network traffic in recent years. However, it suffers serious imbalance distribution of data if the traffic model skews towards the modeling in the benign direction, because only a small portion of traffic is malicious, while most network traffic is benign. That is the reason why the authors wrote this manuscript. Methods. We propose a cost-sensitive approach to improve the HTTP traffic detection performance with imbalanced data and also present a character-level abstract feature extraction approach that can provide features with clear decision boundaries in addition. Finally, we design a spark-based HTTP traffic detection system based on these two approaches. Results. The methods proposed in this paper work well in imbalanced datasets. Compared to other methods, the experiment results indicate that our system has F1-score in a high precision. Conclusion. For imbalanced HTTP traffic detection, we confirmed that the method of feature extraction and the cost function is very effective. In the future, we may focus on how to use the cost function to further improve detection performance.

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