• International Journal of Internet, Broadcasting and Communication
• /
• v.7 no.2
• /
• pp.149-154
• /
• 2015

Biometric techniques for authentication using body parts such as a fingerprint, face, iris, voice, finger-vein and also photoplethysmography have become increasingly important in the personal security field, including door access control, finance security, electronic passport, and mobile device. Finger-vein images are now used to human identification, however, difficulties in recognizing finger-vein images are caused by capturing under various conditions, such as different temperatures and illumination, and noise in the acquisition camera. The human photoplethysmography is also important signal for human identification. In this paper To increase the recognition rate, we develop camera based identification method by combining finger vein image and photoplethysmography signal. We use a compact CMOS camera with a penetrating infrared LED light source to acquire images of finger vein and photoplethysmography signal. In addition, we suggest a simple pattern matching method to reduce the calculation time for embedded environments. The experimental results show that our simple system has good results in terms of speed and accuracy for personal identification compared to the result of only finger vein images.

• Convergence Security Journal
• /
• v.16 no.1
• /
• pp.81-87
• /
• 2016

In this paper, we propose a forgery/falsification detection technique of web site using the images. The proposed system captures images of the web site when a user accesses to the forgery/falsification web site that has the financial information deodorizing purpose. The captured images are compared with those of normal web site images to detect forgery/falsification. The proposed system calculates similarity factor of normal site image with captured one to detect whether the site is normal or not. If it is determined as normal, analysis procedure is finished. But if it is determined as abnormal, a message informs the user to prevent additional financial information spill and further accidents from the forgery web site.

• Convergence Security Journal
• /
• v.11 no.6
• /
• pp.17-23
• /
• 2011

In this paper, we propose mobile Augmented Reality(AR) for smart learning system which is advanced e-learning. AR is technology that seamlessly overlays computer graphics on the real world. AR has become widely available because of mobile AR. Mobile AR is possible to get information from real world anytime, anywhere. Nowadays, there are various areas using AR such as entertainment, marketing, location-based AR. One of the most promising areas is education. AR in education shows lifelike images to users for realism. It's a good way for improving concentration and attention. We utilize only a camera for image-based AR without other sensor.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2015.10a
• /
• pp.27-29
• /
• 2015

Based on IALA guidelines, the fundamental requirements of radar system for vessel traffic services are analyzed in this paper. target separation, target position accuracy, target track accuracy of X-band radar and recommended test conditions are analyzed. Also, in order to check if it satisfies the requirement of target position accuracy from IALA guideline, the test is carried out through processing of radar raw image acquired at VTS center.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.51-62
• /
• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.48-54
• /
• 2023

Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.183-189
• /
• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.45 no.3
• /
• pp.177-187
• /
• 2009

This paper describes a automatic video surveillance system(AVSS) with long range and 360$^{\circ}$ coverage that is automatically rotated in an elevation over azimuth mode in response to the TTM(tracked target message) signal of vessels tracked by ARPA(automatic radar plotting aids) radar. This AVSS that is a video security and tracking system supported by ARPA radar, CCTV(closed-circuit television) camera system and other sensors to automatically identify and track, detect the potential dangerous situations such as collision accidents at sea and berthing/deberthing accidents in harbor, can be used in monitoring the illegal fishing vessels in inshore and offshore fishing ground, and in more improving the security and safety of domestic fishing vessels in EEZ(exclusive economic zone) area. The movement of the target vessel chosen by the ARPA radar operator in the AVSS can be automatically tracked by a CCTV camera system interfaced to the ECDIS(electronic chart display and information system) with the special functions such as graphic presentation of CCTV image, camera position, camera azimuth and angle of view on the ENC, automatic and manual controls of pan and tilt angles for CCTV system, and the capability that can replay and record continuously all information of a selected target. The test results showed that the AVSS developed experimentally in this study can be used as an extra navigation aid for the operator on the bridge under the confusing traffic situations, to improve the detection efficiency of small targets in sea clutter, to enhance greatly an operator s ability to identify visually vessels tracked by ARPA radar and to provide a recorded history for reference or evidentiary purposes in EEZ area.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.4
• /
• pp.835-845
• /
• 2019

File Carving is a technique for attempting to recover a file without metadata, such as a formated storage media or a damaged file system, and generally looks for a specific header / footer signature and data structure of the file. However, file carving is faced with the problem of recovering fragmented files for a long time, and it is very important to propose a solution for digital forensics because important files are relatively fragmented. To overcome these limitations, various carving techniques and tools are continuously being developed, and data sets from various researches and institutions are provided for functional verification. However, existing data sets are ineffective in verifying tools because of their limited environmental conditions. Therefore, this paper refers to the importance of fragmented file carving and develops 16 images for carving tool verification based on scenarios. The developed images' carving rate and accuracy of each media is shown through Foremost which is well known as a commercial carving tool.

• Journal of Radiation Industry
• /
• v.5 no.2
• /
• pp.185-189
• /
• 2011

As nuclear industry has been developed, a various types of radiological contamination has occurred. After 9.11 terror in U.S.A., it has been concerned that terrorists' active area has been enlarged to use nuclear or radioactive substance. Recently, the most powerful earth-quake stroke, which triggered a massive tsunami in Japan and then Fukushima nuclear power plant reactor has suffered from a serious accident in history. The Fukushima reactor accident has occurred an anxiety of radiation leaks and about 170,000 people have been evacuated from the accidental area near the nuclear power plant. For these reasons, a social chaos can be occurred if radiological contamination occurs to the supply system for the drinking water. As such, the establishment of the radiation monitoring system for the city main water system is compelling for the national security. In this study, a feasibility test of radiation monitoring system which consists of unified hybrid-type radiation detectors was experimented for multi detection system by using gamma-ray imaging. The hybrid-type radiation sensors were fabricated with CsI(Tl) scintillators and photodiodes. A preamplifier and amplifier was also fabricated and assembled with the sensor in the shielding case. For the preliminary test of detection of radiological contamination in the river, multi CsI(Tl)-PIN photodiode radiation detectors and $^{137}Cs$ gamma-ray source were used. The DAQ was done by Linux based ROOT program and NI DAQ system with Labview program. The simulated contamination was assumed to be occurred at Gapcheon river in Daejeon city. Multi CsI(Tl)-PIN photodiode radiation detectors were positioned at the Gapcheon river side. Assuming that the radiological contaminations flows in the river the $^{137}Cs$ gamma-ray source has been moved and then, the contamination region was reconstructed.