• The Journal of the Acoustical Society of Korea
• /
• v.27 no.8
• /
• pp.427-434
• /
• 2008

In this paper, a transcoding algorithm for SMV and AMR speech coder is proposed. In the application requiring the interoperability of different networks, two speech coders must work together with the structure of cascaded connection, tandem. The tandem which is one of the simplest methods has several problems such as long delay, high complexity and the quality degradation due to twice complete encoding/decoding process. These problems can be solved by using transcoding algorithm. The proposed algorithm consists of LSP (Line Spectral Pair) conversion, pitch delay conversion, and fast fixed codebook search. The evaluation results show that the proposed algorithm achieves equivalent speech quality to that of tandem with reduced computational complexity and delay.

• Journal of IKEEE
• /
• v.28 no.3
• /
• pp.412-418
• /
• 2024

This paper proposes an effective method for detecting hacking attacks in automotive CAN bus using the RANSAC (Random Sample Consensus) algorithm. Conventional deep learning-based detection techniques are difficult to be applied to resource-constrained environments such as vehicles. In this paper, the attack detection performance in vehicular CAN communication has been improved by utilizing the lightweight nature and efficiency of the RANSAC algorithm. The RANSAC algorithm can perform effective detection with minimal computational resources, providing a practical hacking detection solution for vehicles.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.48 no.2
• /
• pp.105-115
• /
• 2011

User authentication service is an absolutely necessary condition while securely implementing an IT service system. It allows for valid users to securely log-in the system and even to access valid resources from database. For efficiently and securely authenticating users, smart-card has been used as a popular tool because of its convenience and popularity. Furthermore the smart-card can maintain its own power for computation and storage, which makes it easier to be used in all types of authenticating environment that usually needs temporary storage and additional computation for authenticating users and server. First, in 1981, Lamport has designed an authentication service protocol based on user's smart-card. However it has been criticized in aspects of efficiency and security because it uses hash chains and the revealment of server's secret values are not considered. Over the years, many smart-card based authentication service protocol have been designed. Very recently, Xu, Zhu, Feng have suggested a provable and secure smart-card based authentication protocol. In this paper, first, we define all types of attacks in the smart-card based authentication service. According to the defined attacks, however, the protocol by Xu, Zhu, Feng is weak against an attack that an attacker with secret values of server is able to impersonate a valid user without knowing password and secret values of user. An efficient and secure countermeasure is suggested, then the security is analyzed.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.2
• /
• pp.103-116
• /
• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• Journal of KIISE:Databases
• /
• v.34 no.1
• /
• pp.1-17
• /
• 2007

As XML is becoming a de facto standard for distribution and sharing of information, the need for an efficient yet secure access of XML data has become very important. To enforce the fine-level granularity requirement, authorization models for regulating access to XML documents use XPath which is a standard for specifying parts of XML data and a suitable language for both query processing. An access control environment for XML documents and some techniques to deal with authorization priorities and conflict resolution issues are proposed. Despite this, relatively little work has been done to enforce access controls particularly for XML databases in the case of query access. Developing an efficient mechanism for XML databases to control query-based access is therefore the central theme of this paper. This work is a proposal for an efficient yet secure XML access control system. The basic idea utilized is that a user query interaction with only necessary access control rules is modified to an alternative form which is guaranteed to have no access violations using tree-aware metadata of XML schemes and set operators supported by XPath 2.0. The scheme can be applied to any XML database management system and has several advantages over other suggested schemes. These include implementation easiness, small execution time overhead, fine-grained controls, and safe and correct query modification. The experimental results clearly demonstrate the efficiency of the approach.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.12
• /
• pp.623-634
• /
• 2016

Most of existing frequent pattern mining methods address time efficiency and greatly rely on the primary memory. However, in the era of big data, the size of real-world databases to mined is exponentially increasing, and hence the primary memory is not sufficient enough to mine for frequent patterns from large real-world data sets. To solve this problem, there are some researches for frequent pattern mining method based on disk, but the processing time compared to the memory based methods took very time consuming. There are some researches to improve scalability of frequent pattern mining, but their processes are very time consuming compare to the memory based methods. In this paper, we present PPFP as a novel disk-based approach for mining frequent itemset from big data; and hence we reduced the main memory size bottleneck. PPFP algorithm is based on FP-growth method which is one of the most popular and efficient frequent pattern mining approaches. The mining with PPFP consists of two setps. (1) Constructing an IFP-tree: After construct FP-tree, we assign index number for each node in FP-tree with novel index numbering method, and then insert the indexed FP-tree (IFP-tree) into disk as IFP-table. (2) Mining frequent patterns with PPFP: Mine frequent patterns by expending patterns using stack based PUSH-POP method (PPFP method). Through this new approach, by using a very small amount of memory for recursive and time consuming operation in mining process, we improved the scalability and time efficiency of the frequent pattern mining. And the reported test results demonstrate them.

• Journal of the Korea Computer Graphics Society
• /
• v.26 no.1
• /
• pp.17-25
• /
• 2020

We present a novel multi-core CPU based parallel algorithm for the cell-connectivity information extraction algorithm, which is one of the preprocessing steps for volume rendering of unstructured grid data. We first check the synchronization issues when parallelizing the prior serial algorithm naively. Then, we propose a 3-step parallel algorithm that achieves high parallelization efficiency by removing synchronization in each step. Also, our 3-step algorithm improves the cache utilization efficiency by increasing the spatial locality for the duplicated triangle test process, which is the core operation of building cell-connectivity information. We further improve the efficiency of our parallel algorithm by employing a memory pool for each thread. To check the benefit of our approach, we implemented our method on a system consisting of two octa-core CPUs and measured the performance. As a result, our method shows continuous performance improvement as we add threads. Also, it achieves up to 82.9 times higher performance compared with the prior serial algorithm when we use thirty-two threads (sixteen physical cores). These results demonstrate the high parallelization efficiency and high cache utilization efficiency of our method. Also, it validates the suitability of our algorithm for large-scale unstructured data.

• Spatial Information Research
• /
• v.20 no.1
• /
• pp.91-103
• /
• 2012

Recently, there has been much interest in the middleware that can smoothly acquire and analyze Geosensor information which includes sensor readings, location, and its surrounding spatial information. In relation to development of the middleware, researchers have proposed various algorithms for energy-efficient information filtering in Geosensor networks and have proposed Geosensor web technologies which can efficiently mash up sensor readings with spatial information on the web, also. The filtering algorithms and Geosensor Web technologies have contributions on energy-efficiency and OpenAPI, however the algorithms and technologies could not support easy and rapid development of u-GIS applications that need various Geosensor networks. Therefore, we propose a new Geosensor network middleware that can dramatically reduce the time and cost required for development of u-GIS applications that integrate heterogeneous Geosensor networks. The proposed middleware has several merits of being capable of acquiring heterogeneous Geosensor information using the standard SWE and an extended SQL, optimally performing various attribute and spatial operators, and easily integrating various Geosensor networks. Finally, we clarify our middleware's distinguished features by developing a prototype that can monitor environmental information in realtime using spatial information and various sensor readings of temperature, humidity, illumination, imagery, and location.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.5
• /
• pp.9-19
• /
• 2012

As the clock frequency increases, CPU performance improves continuously. However, power and thermal problems in the CPU become more serious as the clock frequency increases. For this reason, utilizing the GPU to reduce the workload of the CPU becomes one of the most popular methods in recent high-performance computer systems. The GPU is a specialized processor originally designed for graphics processing. Recently, the technologies such as CUDA which utilize the GPU resources more easily become popular, leading to the improved performance of the computer system by utilizing the CPU and GPU simultaneously in executing various kinds of applications. In this work, we analyze the temperature and the energy efficiency of the computer system where the CPU and the GPU are utilized simultaneously, to figure out the possible problems in upcoming high-performance computer systems. According to our experimentation results, the temperature of both CPU and GPU increase when the application is executed on the GPU. When the application is executed on the CPU, CPU temperature increases whereas GPU temperature remains unchanged. The computer system shows better energy efficiency by utilizing the GPU compared to the CPU, because the throughput of the GPU is much higher than that of the CPU. However, the temperature of the system tends to be increased more easily when the application is executed on the GPU, because the GPU consumes more power than the CPU.

• Journal of Broadcast Engineering
• /
• v.16 no.2
• /
• pp.339-349
• /
• 2011

IBDI (Internal Bit Depth Increase) is able to significantly improve the coding efficiency of high definition video compression by increasing the bit depth (or precision) of internal arithmetic operation. However the scheme also increases required internal memory for storing decoded reference frames and this can be significant for higher definition of video contents. So, the reference frame memory compression method is proposed to reduce such internal memory requirement. The reference memory compression is performed on 4x4 block called the processing unit to compress the decoded image using the correlation of nearby pixel values. This method has successively reduced the reference frame memory while preserving the coding efficiency of IBDI. However, additional information of each processing unit has to be stored also in internal memory, the amount of additional information could be a limitation of the effectiveness of memory compression scheme. To relax this limitation of previous memory compression scheme, we propose a selective merging-based reference frame memory compression algorithm, dramatically reducing the amount of additional information. Simulation results show that the proposed algorithm provides much smaller overhead than that of the previous algorithm while keeping the coding efficiency of IBDI.