• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.137-148
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• 2012

Cloud service users request dedicated virtual computing resource from the cloud service provider to process jobs in independent environment from other users. To optimize this process with automated method, in this paper we proposed a framework for workflow scheduling in the cloud environment, in which the core component is the middleware called broker mediating the interaction between users and cloud service providers. To process jobs in on-demand and virtualized resources from cloud service providers, many papers propose scheduling algorithms that allocate jobs to virtual machines which are dedicated to one machine one job. With this method, the isolation of being processed jobs is guaranteed, but we can't use each resource to its fullest computing capacity with high efficiency in resource utilization. This paper therefore proposed a cost-efficient job scheduling algorithm which maximizes the utilization of managed resources with increasing the degree of multiprogramming to reduce the number of needed virtual machines; consequently we can save the cost for processing requests. We also consider the performance degradation in proposed scheme with thrashing and context switching. By evaluating the experimental results, we have shown that the proposed scheme has better cost-performance feature compared to an existing scheme.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.249-254
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• 2011

FGS (fine granularity scalability) supporting scalability in MPEG-4 Part 2 is a scalable video coding scheme that provides bit-rate adaptation to varying network bandwidth thereby achieving of its optimal video quality. In this paper, we proposed FGS coding scheme which performs one more bit-plane coding for residue signal occured in the enhancement-layer of the basic FGS coding scheme. The experiment evaluated in terms of video quality scalability of the proposed FGS coding scheme by comparing with FGS coding scheme of the MPEG-4 verification model (VM-FGS). The comparison was conducted by analysis of PSNR values of three tested video sequences. The results showed that when using rate control algorithm VM5+, the proposed FGS coding scheme obtained Y, U, V PSNR of 0.4 dB, 9.4 dB, 9 dB averagely higher and when using fixed QP value 17, obtained Y, U, V PSNR of 4.61 dB, 20.21 dB, 16.56 dB averagely higher than the existing VM-FGS. From results, we found that the proposed FGS coding scheme has higher video quality scalability to be able to achieve video quality from minimum to maximum than VM-FGS.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.8
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• pp.80-88
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• 2007

High adaptability and scalability are two critical issues in implementing a very complex system in a single chip. To obtain high adaptability and scalability, novel system design methodology known as communication-based system design has gained large attention from SoC designers. NoC (Network-on-Chip) is such an on-chip communication-based design approach for the next generation SoC design. To provide high adaptability and scalability, NoCs employ network interfaces and routers as their main communication structures and transmit and receive packetized data over such structures. However, data packetization, and routing overhead in terms of run time and area may cost too much compared with conventional SoC communication structure. Therefore, in this research, we propose a novel methodology which automatically generates a hybrid communication structure. In this work, we map traditional pin-to-pin wiring structure for frequent and timing critical communication, and map flexible and scalable structure for infrequent, or highly variable communication patterns. Even though, we simplify the communication structure significantly through our algorithm the connectivity or the scalability of the communication modules are almost maintained as the original NoC design. Using this method, we could improve the timing performance by 49.19%, and the area taken by the communication structure has been reduced by 24.03%.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.173-184
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• 2002

This paper discuss an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, Kim et al. suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multidimensional index using a feature vector as indexing attributes. For query processing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verify the superiority of our approach, we perform extensive experiments. The results reveal that our approach achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.1
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• pp.1-10
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• 2012

Mobility and quality of service (QoS) are becoming the more important issues in wireless communications. The traditional Internet service is expanding into new access media and applications. Since wireless communication services are accompanied by frequent handovers at remote sites, scalable and fast handover has become a prerequisite for ubiquitous communication. In this paper, the differentiated service (Diffserv) model is deployed in heterogeneous proxy mobile IPv6 (PMIPv6) networks to satisfy the QoS guaranteed service and fast handover requirements. The operational procedures for QoS guaranteed global roaming are presented. In addition, QoS management and handover cost evaluation schemes based on a mobile host's movement scope are proposed. This paper analyzes the reduction in handover delay in a network-based localized mobility management framework. We propose and analyze a PMIPv6 optimized with a global mobile access gateway (G-MAG), which is a network-based entity, to further improve the handover performance in terms of handover delay while maintaining minimal signaling overhead in the air interface among converged heterogeneous wireless networks. The handover signaling procedures with host-based MIPv6 are compared with network-based proxy MIPv6 (PMIPv6) and fast PMIPv6 assisted by G-MAG to show how much handover delay reduction can be achieved. Analytical results show that the handover delay is significantly reduced.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.2
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• pp.81-89
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• 2008

Wideband speech, characterized by a bandwidth of about 7 kHz (50-7000 Hz), provides a substantial quality improvement in terms of naturalness and intelligibility. Although higher data rates are required, it has extended its application to audio and video conferencing, high-quality multimedia communications in mobile links or packet-switched transmissions, and digital AM broadcasting. In this paper, we present a new bandwidth-scalable coder for wideband speech and audio signals. The proposed coder spits 8kHz signal bandwidth into two narrow bands, and different coding schemes are applied to each band. The lower-band signal is coded using the ITU-T G.729/G.729E coder, and the higher-band signal is compressed using a new algorithm based on the gammatone filter bank with an invertible auditory model. Due to the split-band architecture and completely independent coding schemes for each band, the output speech of the decoder can be selected to be a narrowband or wideband according to the channel condition. Subjective tests showed that, for wideband speech and audio signals, the proposed coder at 14.2/18 kbit/s produces superior quality to ITU-T 24 kbit/s G.722.1 with the shorter algorithmic delay.

• Journal of KIISE:Databases
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• v.29 no.3
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• pp.180-192
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• 2002

We investigate the effect of the data skew of join attributes on the performance of a pipelined multi-way hash join method, and propose two new harsh join methods in the shared-nothing multiprocessor environment. The first proposed method allocates buckets statically by round-robin fashion, and the second one allocates buckets dynamically via a frequency distribution. Using harsh-based joins, multiple joins can be pipelined to that the early results from a join, before the whole join is completed, are sent to the next join processing without staying in disks. Shared nothing multiprocessor architecture is known to be more scalable to support very large databases. However, this hardware structure is very sensitive to the data skew. Unless the pipelining execution of multiple hash joins includes some dynamic load balancing mechanism, the skew effect can severely deteriorate the system performance. In this parer, we derive an execution model of the pipeline segment and a cost model, and develop a simulator for the study. As shown by our simulation with a wide range of parameters, join selectivities and sizes of relations deteriorate the system performance as the degree of data skew is larger. But the proposed method using a large number of buckets and a tuning technique can offer substantial robustness against a wide range of skew conditions.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.397-406
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• 2014

In order to answer the questions successfully on behalf of the human in DeepQA environments such as Jeopardy! of the American quiz show, the computer is required to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a scalable spatial reasoning algorithm for deriving efficiently new directional and topological relations using the MapReduce framework, one of well-known parallel distributed computing environments. The proposed reasoning algorithm assumes as input a large-scale spatial knowledge base including CSD-9 directional relations and RCC-8 topological relations. To infer new directional and topological relations from the given spatial knowledge base, it performs the cross-consistency checks as well as the path-consistency checks on the knowledge base. To maximize the parallelism of reasoning computations according to the principle of the MapReduce framework, we design the algorithm to partition effectively the large knowledge base into smaller ones and distribute them over multiple computing nodes at the map phase. And then, at the reduce phase, the algorithm infers the new knowledge from distributed spatial knowledge bases. Through experiments performed on the sample knowledge base with the MapReduce-based implementation of our algorithm, we proved the high performance of our large-scale spatial reasoner.

• Journal of KIISE
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• v.43 no.10
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• pp.1115-1123
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• 2016

Assumption-based truth maintenance system (ATMS) is a tool that maintains the reasoning process of inference engine. It also supports non-monotonic reasoning based on dependency-directed backtracking. Bookkeeping all the reasoning processes allows it to quickly check and retract beliefs and efficiently provide solutions for problems with large search space. However, the amount of data has been exponentially grown recently, making it impossible to use a single machine for solving large-scale problems. The maintaining process for solving such problems can lead to high computation cost due to large memory overhead. To overcome this drawback, this paper presents an approach towards incrementally maintaining the reasoning process of inference engine on cluster using Spark. It maintains data dependencies such as assumption, label, environment and justification on a cluster of machines in parallel and efficiently updates changes in a large amount of inferred datasets. We deployed the proposed ATMS on a cluster with 5 machines, conducted OWL/RDFS reasoning over University benchmark data (LUBM) and evaluated our system in terms of its performance and functionalities such as assertion, explanation and retraction. In our experiments, the proposed system performed the operations in a reasonably short period of time for over 80GB inferred LUBM2000 dataset.