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The Real Time Infrared Image Acquisition and Processing System Design Based on FPGA
Fan Jianying,Cui Xin,Fan Zhigang,Feng Yao 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.2
Field programmable gate array (FPGA) has the characteristics of high speed, low power consumption, high integration, flexibility and, small size, etc. In this paper, I design a real time infrared image processing and display system based on FPGA for the requirement of real time infrared image processing, which will realize the transmission, transformation, and storage of the image information, and then complete the infrared image edge detection based on Sobel algorithm by using this system as a platform. At last, the advantages of infrared image processing with this system compared with other methods will be verified in this paper. The experimental results show that the system spends 11.44ms on processing the colorful image whose resolution is 640480.The system has realized the real time, high speed, stable and reliable acquisition, processing and display of infrared image and we can realize the infrared target feature extraction, recognition and tracking when we combine the system with other algorithms.
Design of Test Platform for Concentrator based on Virtual Electric Energy Meter
Jianying Fan,Hui Sun,Yang Wang,Dongqing Shi 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.9
For the current test of concentrator relay routing learning function in the laboratory, based on the hardware cost and the deployment environment, unable to realize large-scale electric energy meter group network, and real scene environment cannot be simulated. In order to solve these problems, the design of test platform for concentrator based on virtual electric energy meter is proposed in this paper. The design aims to simulate the communication routing network algorithm of electric energy meter and help the communication instruction to establish an optimal topology network which through using the specified data source, target source and the route address. So the concentrator can get the specific data through this preset network. This paper have also observed and analyzed the hierarchy order topology and compared with the autonomous learning topology of the concentrator relay routing algorithm. Then verify the deviation between communication routing topology and preset value, and eliminate the deviation. At the same time, verify whether the relay routing algorithm can get the optimal communication topology network.
Yu-lian Tang,Xiao-ming Zhang,Zhi-gang Yang,Yu-cheng Huang,Tian-wu Chen,Yan-li Chen,Fan Chen,Nan-lin Zeng,Rui Li,Jiani Hu 대한영상의학회 2017 Korean Journal of Radiology Vol.18 No.4
Objective: To explore the association between the blood oxygenation T2* values of resectable esophageal squamous cell carcinomas (ESCCs) and tumor stages. Materials and Methods: This study included 48 ESCC patients and 20 healthy participants who had undergone esophageal T2*-weighted imaging to obtain T2* values of the tumors and normal esophagi. ESCC patients underwent surgical resections less than one week after imaging. Statistical analyses were performed to identify the association between T2* values of ESCCs and tumor stages. Results: One-way ANOVA and Student-Newman-Keuls tests revealed that the T2* value could differentiate stage T1 ESCCs (17.7 ± 3.3 ms) from stage T2 and T3 tumors (24.6 ± 2.7 ms and 27.8 ± 5.6 ms, respectively; all ps < 0.001). Receiver operating curve (ROC) analysis showed the suitable cutoff T2* value of 21.3 ms for either differentiation. The former statistical tests demonstrated that the T2* value could not differentiate between stages T2 and T3 (24.6 ± 2.7 ms vs. 27.8 ± 5.6 ms, respectively, p > 0.05) or between N stages (N1 vs. N2 vs. N3: 24.7 ± 6.9 ms vs. 25.4 ± 4.5 ms vs. 26.8 ± 3.9 ms, respectively; all ps > 0.05). The former tests illustrated that the T2* value could differentiate anatomic stages I and II (18.8 ± 4.8 ms and 26.9 ± 5.9 ms, respectively) or stages I and III (27.3 ± 3.6 ms). ROC analysis depicted the same cutoff T2* value of 21.3 ms for either differentiation. In addition, the Student’s t test revealed that the T2* value could determine grouped T stages (T0 vs. T1–3: 17.0 ± 2.9 ms vs. 25.2 ± 6.2 ms; T0–1 vs. T2–3: 17.3 ± 3.0 ms vs. 27.1 ± 5.3 ms; and T0–2 vs. T3: 18.8 ± 4.2 ms vs. 27.8 ± 5.6 ms, all ps < 0.001). ROC analysis indicated that the T2* value could detect ESCCs (cutoff, 20 ms), and discriminate between stages T0–1 and T2–3 (cutoff, 21.3 ms) and between T0–2 and T3 (cutoff, 20.4 ms). Conclusion: The T2* value can be an additional quantitative indicator for detecting ESCC except for stage T1 cancer, and can preoperatively discriminate between some T stages and between anatomic stages of this tumor.