• Journal of Computing Science and Engineering
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• v.4 no.3
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• pp.207-224
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• 2010

This paper presents a compression scheme for mesh geometry, which is suitable for mobile graphics. The main focus is to enable real-time decoding of compressed vertex positions while providing reasonable compression ratios. Our scheme is based on local quantization of vertex positions with mesh partitioning. To prevent visual seams along the partitioning boundaries, we constrain the locally quantized cells of all mesh partitions to have the same size and aligned local axes. We propose a mesh partitioning algorithm to minimize the size of locally quantized cells, which relates to the distortion of a restored mesh. Vertex coordinates are stored in main memory and transmitted to graphics hardware for rendering in the quantized form, saving memory space and system bus bandwidth. Decoding operation is combined with model geometry transformation, and the only overhead to restore vertex positions is one matrix multiplication for each mesh partition. In our experiments, a 32-bit floating point vertex coordinate is quantized into an 8-bit integer, which is the smallest data size supported in a mobile graphics library. With this setting, the distortions of the restored meshes are comparable to 11-bit global quantization of vertex coordinates. We also apply the proposed approach to compression of vertex attributes, such as vertex normals and texture coordinates, and show that gains similar to vertex geometry can be obtained through local quantization with mesh partitioning.

• Journal of the Korean Mathematical Society
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• v.51 no.2
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• pp.239-250
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• 2014

Let R be a commutative Noetherian (not necessarily local) ring, I an ideal of R and M a finitely generated R-module. In this paper, by computing the local cohomology modules and Ext-modules via the injective resolution of M, we proved that, if for an integer t > 0, dim$_RH_I^i(M){\leq}k$ for ${\forall}i$ < t, then $$\displaystyle\bigcup_{i=0}^{j}(Ass_RH_I^i(M))_{{\geq}k}=\displaystyle\bigcup_{i=0}^{j}(Ass_RExt_R^i(R/I^n,M))_{{\geq}k}$$ for ${\forall}j{\leq}t$ and ${\forall}n$ >0. This shows that${\bigcup}_{n>0}(Ass_RExt_R^i(R/I^n,M))_{{\geq}k}$ is a finite set for ${\forall}i{\leq}t$. Also, we prove that $\displaystyle\bigcup_{i=1}^{r}(Ass_RM/(x_1^{n_1},x_2^{n_2},{\ldots},x_i^{n_i})M)_{{\geq}k}=\displaystyle\bigcup_{i=1}^{r}(Ass_RM/(x_1,x_2,{\ldots},x_i)M)_{{\geq}k}$ if $x_1,x_2,{\ldots},x_r$ is M-sequences in dimension > k and $n_1,n_2,{\ldots},n_r$ are some positive integers. Here, for a subset T of Spec(R), set $T_{{\geq}i}=\{{p{\in}T{\mid}dimR/p{\geq}i}\}$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4429-4447
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• 2018

Local binary descriptors are well-suited for many real-time and/or large-scale computer vision applications, while their low computational complexity is usually accompanied by the limitation of performance. In this paper, we propose a new optimization framework, RLDB (Robust-LDB), to improve a typical region-based binary descriptor LDB (local difference binary) and maintain its computational simplicity. RLDB extends the multi-feature strategy of LDB and applies a more complete region-comparing configuration. A cascade bit selection method is utilized to select the more representative patterns from massive comparison pairs and an online learning strategy further optimizes descriptor for each specific patch separately. They both incorporate LDP (linear discriminant projections) principle to jointly guarantee the robustness and distinctiveness of the features from various scales. Experimental results demonstrate that this integrated learning framework significantly enhances LDB. The improved descriptor achieves a performance comparable to floating-point descriptors on many benchmarks and retains a high computing speed similar to most binary descriptors, which better satisfies the demands of applications.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.501-506
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• 2008

The classical snake algorithm has a problem in detecting the boundary of an object with deep concavities. While the GVF method can successfully detect boundary concavities, it consumes a lot of time computing the energy map. In this paper, we propose an algorithm to reduce the computation time and improve performance in detecting the boundary of an object with high concavity. We define the degree of complexity of object boundary as the local curvature. If the value of the local curvature is greater than a threshold value, new snake points are added. Simulation results on several different test images show that our method performs well in detecting object boundary and requires less computation time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4A
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• pp.528-538
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• 2000

Mobile computing system requires both precise identification and secure authentication scheme on remote mobile entities, which is based on the distributed mobile node. In this paper, existing discrete logarithm based $Schnorr^{[7]}$ like entity authentication schemes are improved by the analysis of performance and security on the hi-directional interactive proofs. And $EIGamal^{[14]}$ like efficient authentication schemes are also proposed. Then, these are enhanced with oblivious transfer based mono directional authentication schemes based on trusted third party for applying to the mobile agent based computing systems. Therefore, proposed schemes provide compatible performance and safety on mobile entity authentication processes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.123-128
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• 2009

In ubiquitous computing environments, service discovery to search for an available service is an important issue. In this paper, we propose an efficient service discovery scheme that is combined a node id-based clustering service discovery scheme and a P2P caching-based information spreading scheme. To search quickly a service, proposed scheme store key information in neighbor's local cache and search services using it's information. We do not use a central look up server and do not rely on flooding. Through simulation, we show that the proposed scheme improves the performance of response time and network load compared to other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3182-3202
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• 2015

Flexibly expanding the storage capacity required to process a large amount of rapidly increasing unstructured log data is difficult in a conventional computing environment. In addition, implementing a log processing system providing features that categorize and analyze unstructured log data is extremely difficult. To overcome such limitations, we propose and design a MongoDB-based unstructured log processing system (MdbULPS) for collecting, categorizing, and analyzing log data generated from banks. The proposed system includes a Hadoop-based analysis module for reliable parallel-distributed processing of massive log data. Furthermore, because the Hadoop distributed file system (HDFS) stores data by generating replicas of collected log data in block units, the proposed system offers automatic system recovery against system failures and data loss. Finally, by establishing a distributed database using the NoSQL-based MongoDB, the proposed system provides methods of effectively processing unstructured log data. To evaluate the proposed system, we conducted three different performance tests on a local test bed including twelve nodes: comparing our system with a MySQL-based approach, comparing it with an Hbase-based approach, and changing the chunk size option. From the experiments, we found that our system showed better performance in processing unstructured log data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5269-5286
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• 2018

Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.97-106
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• 2023

Citizens' demand for public CCTVs continues to rise, along with an increase in variouscrimes and social problems in cities. In line with the needs of citizens, the Seoul Metropolitan Government began installing CCTV cameras in 2010, and the number of new installations has increased by over 10% each year. As the large surveillance system represents a substantial budget item for the city, decision-making on location selection should be guided by reasonable standards. The purpose of this study is to improve the existing related models(such as public CCTV priority location analysis manuals) to establish the methodology foranalyzing priority regions ofSeoul-type public CCTVs and propose new mid- to long-term installation goals. Additionally, using the improved methodology, we determine the CCTV priority status of 25 autonomous districts across Seoul and calculate the goals. Through its results, this study suggests improvements to existing models by addressing their limitations, such as the sustainability of input data, the conversion of existing general-purpose models to urban models, and the expansion of basic local government-level models to metropolitan government levels. The results can also be applied to other metropolitan areas and are used by the Seoul Metropolitan Government in its CCTV operation policy