• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.14 no.1
• /
• pp.113-119
• /
• 2004

Exponentiation is widely used in practical applications related with cryptography, and as the discrete log is easily solved in case of a low exponent n, a large exponent n is needed for a more secure system. However. since the time complexity for exponentiation algorithm increases in proportion to the n figure, the development of an exponentiation algorithm that can quickly process the results is becoming a crucial problem. In this paper, we propose a parallel exponentiation algorithm which can reduce the number of rounds with a fixed number of processors, where the field elements are in GF($2^m$), and also analyzed the round bound of the proposed algorithm. The proposed method uses window method which divides the exponent in a particular bit length and make idle processors in window value computation phase to multiply some terms of windows where the values are already computed. By this way. the proposed method has improved round bound.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.17 no.1
• /
• pp.33-39
• /
• 2007

Finite field multipliers are the basic building blocks in many applications such as error-control coding, cryptography and digital signal processing. Hence, the design of efficient dedicated finite field multiplier architectures can lead to dramatic improvement on the overall system performance. In this paper, a new bit serial structure for a multiplier with low latency in Galois field is presented. To speed up multiplication processing, we divide the product polynomial into several parts and then process them in parallel. The proposed multiplier operates standard basis of $GF(2^m)$ and is faster than bit serial ones but with lower area complexity than bit parallel ones. The most significant feature of the proposed architecture is that a trade-off between hardware complexity and delay time can be achieved.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.18 no.5
• /
• pp.41-47
• /
• 2008

In a network environment, when a user requests a server's service, he/she must pass an examination of user authentication. Through this process, the server can determine if the user can use the provided services and the exact access rights of this user in these services. In these authentication schemes, the security of private information became an important issue. For this reason, many suggestions have been made in order to protect the privacy of users and smart cards have been widely used for authentication systems providing anonymity of users recently. An remote user authentication system using smart cards is a very practical solution to validate the eligibility of a user and provide secure communication. However, there are no studies in attribute-based authentication schemes using smart cards so far. In this paper, we propose a novel user authentication scheme using smart cards based on attributes. The major merits include : (1) the proposed scheme achieves the low-computation requirement for smart cards; (2) user only needs to register once and can use permitted various services according to attributes; (3) the proposed scheme guarantees perfect anonymity to remote server.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.5
• /
• pp.47-58
• /
• 2011

Recently, there are several technologies for protecting information in the lightweight device, One of them, the AES[1] algorithm and CRT mode, is used for numerous services(e,g, OMA DRM, VoIP, IPTV) as encryption technique for preserving confidentiality. Although it is possible that the AES algorithm CRT mode can parallel process transmitting data, IPTV Set-top Box or Mobile Device that uses these streaming service has limited computation-ability. So optimizing crypto algorithm and enhancing its efficiency for those environment have become an important issue. In this paper, we propose implementation method that can improve efficiency of the AES-CRT Mode by improving algorithm logics. Moreover, we prove the performance of our proposal on the mobile device which has limited capability.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.120-130
• /
• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.2
• /
• pp.231-237
• /
• 2020

In this paper, we provide an efficient hybrid video blending scheme to improve the naturalness of composite video in Poisson equation-based composite methods. In image blending process, various blending methods are used depending on the purpose of image composition. The hybrid blending method proposed in this paper has the characteristics that there is no seam in the composite video and the color distortion of the object is reduced by properly utilizing the advantages of Poisson blending and alpha blending. First, after blending the source object by the Poisson blending method, the color difference between the blended object and the original object is compared. If the color difference is equal to or greater than the threshold value, the object of source video is alpha blended and is added together with the Poisson blended object. Simulation results show that the proposed method has not only better naturalness than Poisson blending and alpha blending, but also requires a relatively small amount of computation.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.6
• /
• pp.313-330
• /
• 2021

In an autonomous driving system equipped with a camera and lidar, depth completion techniques enable dense depth estimation. In particular, using self-supervised learning it is possible to train the depth completion network even without ground truth. In actual autonomous driving, such depth completion should have very short latency as it is the input of other algorithms. So, rather than complicate the network structure to increase the accuracy like previous studies, this paper focuses on network latency. We design a U-Net type network with RegNet encoders optimized for GPU computation. Instead, this paper presents several techniques that can increase accuracy during the process of self-supervised learning. The proposed techniques increase the robustness to unreliable lidar inputs. Also, they improve the depth quality for edge and sky regions based on the semantic information extracted in advance. Our experiments confirm that our model is very lightweight (2.42 ms at 1280x480) but resistant to noise and has qualities close to the latest studies.

• Journal of IKEEE
• /
• v.26 no.2
• /
• pp.226-231
• /
• 2022

The concept of information entropy has been widely applied in various fields. Recently, in the field of image processing, many technologies applying the concept of information entropy have been developed. As the importance and demand of computer vision technologies increase in modern industry, real-time processing must be possible in order for image processing technologies to be efficiently applied to modern industries. Extracting the entropy value of an image is difficult to process in real-time due to the complexity of computation in software, and a hardware structure of an image entropy filter capable of real-time processing has never been proposed. In this paper, we propose for the first time a hardware structure of a histogram-based entropy filter that can be processed in real time using a barrel shifter. The proposed hardware was designed using Verilog HDL, and Xilinx's xczu7ev-2ffvc1156 was set as the target device and FPGA was implemented. As a result of logic synthesis using the Xilinx Vivado program, it has a maximum operating frequency of 750.751 MHz in a 4K UHD high-resolution environment, and it processes more than 30 images per second and satisfies the real-time processing standard.

• The Journal of the Korea Contents Association
• /
• v.22 no.6
• /
• pp.56-68
• /
• 2022

Recently, as the demand for real-time processing increases, studies on a dynamic graph that changes over time has been actively done. There is a connected components processing algorithm as one of the algorithms for analyzing dynamic graphs. GPUs are suitable for large-scale graph calculations due to their high memory bandwidth and computational performance. However, when computing the connected components of a dynamic graph using the GPU, frequent data exchange occurs between the CPU and the GPU during real graph processing due to the limited memory of the GPU. The proposed scheme utilizes the Weighted-Quick-Union algorithm to process large-scale graphs on the GPU. It supports fast connected components computation by applying the size to the connected component label. It computes the connected component by determining the parts to be recalculated and minimizing the data to be transmitted to the GPU. In addition, we propose a processing structure in which the GPU and the CPU execute asynchronously to reduce the data transfer time between GPU and CPU. We show the excellence of the proposed scheme through performance evaluation using real dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.9
• /
• pp.3194-3210
• /
• 2022

Wall obstruction is the main factor leading to the non-line of sight (NLoS) error of indoor localization based on received signal strength indicator (RSSI). Modeling and correcting the path loss of the signals through the wall will improve the accuracy of RSSI localization. Based on electromagnetic wave propagation theory, the reflection and transmission process of wireless signals propagation through the wall is analyzed. The path loss of signals through wall is deduced based on power loss and RSSI definition, and the theoretical model of path loss of signals through wall is proposed. In view of electromagnetic characteristic parameters of the theoretical model usually cannot be accurately obtained, the statistical model of NLoS error caused by the signals through the wall is presented based on the log-distance path loss model to solve the parameters. Combining the statistical model and theoretical model, a hybrid model of path loss of signals through wall is proposed. Based on the empirical values of electromagnetic characteristic parameters of the concrete wall, the effect of each electromagnetic characteristic parameters on path loss is analyzed, and the theoretical model of regional path loss of signals through the wall is established. The statistical model and hybrid model of regional path loss of signals through wall are established by RSSI observation experiments, respectively. The hybrid model can solve the problem of path loss when the material of wall is unknown. The results show that the hybrid model can better express the actual trend of the regional path loss and maintain the pass loss continuity of adjacent areas. The validity of the hybrid model is verified by inverse computation of the RSSI of the extended region, and the calculated RSSI is basically consistent with the measured RSSI. The hybrid model can be used to forecast regional path loss of signals through the wall.