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A Study on Wideband Microstrip Array Antennas Using the Parallel Coupled Lines (펑행 결합 선로를 이용한 광대역 마이크로스트립 배열 안테나에 관한 연구)

  • 김정일;한만군;윤영중
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.26 no.12B
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    • pp.1724-1732
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    • 2001
  • In this paper, a technique for increasing the bandwidth of microstrip array antennas using the parallel coupled lines on a single layer is presented. Four types of wideband microstrip array antenna are designed and the characteristics of each type are analyzed. In addition, an iterative method using a distributed network is proposed to design the parallel coupled lines as a wideband impedance matching network. Measurements show that the proposed antennas provide wider bandwidths ∼1.7 times those of conventional microstirp array antennas, while the sizes of proposed antennal are the same as that of a conventional array. And low cross-polarization level can be obtained through symmetrical locations of the parallel coupled lines section

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Tree based Route Optimization in Nested NEMO Environment (중첩 NEMO 환경에서 트리 기반 라우트 최적화 기법)

  • Lim, Hyung-Jin;Chung, Tai-Myoung
    • Journal of Internet Computing and Services
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    • v.9 no.1
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    • pp.9-19
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    • 2008
  • This paper propose the issue of connecting nested NEMO (Network Nobility) networks to global IPv6 networks, while supporting IPv6 mobility. Specifically, we consider a self-addressing including topology information IPv6-enabled NEMO infrastructure. The proposed self-organization addressing protocol automatically organized mobile routers into free architecture and configuration their global IPv6 addresses. BU(binding update) to MR own HA and internal rouging, hosed on longest prefix matching and soft state routing cache, are specially designed for IPv6-based NEMO. In conclusion, numeric analysis ore conducted to show more efficiency of the proposed routing protocols than other RO (Route Optimization) approaches.

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Design of a 1~10 GHz High Gain Current Reused Low Noise Amplifier in 0.18 ㎛ CMOS Technology

  • Seong, Nack-Gyun;Jang, Yo-Han;Choi, Jae-Hoon
    • Journal of electromagnetic engineering and science
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    • v.11 no.1
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    • pp.27-33
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    • 2011
  • In this paper, we propose a high gain, current reused ultra wideband (UWB) low noise amplifier (LNA) that uses TSMC 0.18 ${\mu}m$ CMOS technology. To satisfy the wide input matching and high voltage gain requirements with low power consumption, a resistive current reused technique is utilized in the first stage. A ${\pi}$-type LC network is adopted in the second stage to achieve sufficient gain over the entire frequency band. The proposed UWB LNA has a voltage gain of 12.9~18.1 dB and a noise figure (NF) of 4.05~6.21 dB over the frequency band of interest (1~10 GHz). The total power consumption of the proposed UWB LNA is 10.1 mW from a 1.4 V supply voltage, and the chip area is $0.95{\times}0.9$ mm.

Design an Algorithm Matching TCP Connection Pairs for Intruder Traceback (침입자 역추적을 위한 TCP 연결 매칭 알고리즘 설계)

  • Kang Hyung-Woo;Hong Soon-Jwa;Lee Dong-Hoon
    • The KIPS Transactions:PartC
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    • v.13C no.1 s.104
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    • pp.11-18
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    • 2006
  • In the field of network defense, a lot of researches are directed toward locating the source of network attacks. When an intruder launches attack not from their own computer but from intermediate hosts that they previously compromised, and these intermediate hosts are called stepping-stones. There we two kinds of traceback technologies : IP packet traceback and connection traceback. We focused on connection traceback in this paper This paper classifies process structures of detoured attack type in stepping stone, designs an algorithm for traceback agent, and implements the traceback system based on the agent

The Sharing Economy Business Model per the Analysis of Value Attributes (공유경제 비즈니스 모델의 가치 요인 분석)

  • Lee, Junmin;Hwang, Junseok;Kim, Jonglip
    • Journal of Information Technology Services
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    • v.15 no.4
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    • pp.153-174
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    • 2016
  • On account of multiple causes, including prolonged global economic crisis, addressing environmental pollution and the advent of hyper-connected society, a new paradigm called 'sharing economy' has rapidly emerged. Many startups have attempted to build promising business model based on the sharing economy concept. Nevertheless, successful cases are still very rare in the global level, except for Uber and Airbnb cases. Therefore, this study analyzes necessary causes and sufficient causes for successful settlements in the market through a comparative case analysis on digital matching firms in the sharing economy businesses. For the case study, we compare five successful cases (Uber, Airbnb, Kickstarter, TaskRabbit and DogVacay), three failure cases (Homejoy, Ridejoy and Tuterspree) and a platform cooperativism case (Juno) in accordance with six value attributes of business model including value proposition, market segment, value chain, cost structure and profit potential, value network and competitive strategy. We apply Boolean method to support controlled comparison and eliminate unnecessary attributes. The Boolean analysis result shows that value proposition, cost structure and profit potential, value network and competitive strategy are the essential attributes. Furthermore, the result indicates that each attribute is a necessary condition, where all four conditions should be met simultaneously in order to be successful. With this result, we discuss essential consideration for those who are planning startup based on the sharing economy business model.

Recognition Performance Enhancement by License Plate Normalization (번호판 정규화에 의한 인식 성능 향상 기법)

  • Kim, Do-Hyeon;Kang, Min-Kyung;Cha, Eui-Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.12 no.7
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    • pp.1278-1290
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    • 2008
  • This paper proposes a preprocessing method and a neural network based character recognizer to enhance the overall performance of the license plate recognition system. First, plate outlines are extracted by virtual line matching, and then the 4 vertexes are obtained by calculating intersecting points of extracted lines. By these vertexes, plate image is reconstructed as rectangle-shaped image by bilinear transform. Finally, the license plate is recognized by the neural network based classifier which had been trained using delta-bar-delta algorithm. Various license plate images were used in the experiments, and the proposed plate normalization enhanced the recognition performance up to 16 percent.

Novel Maritime Wireless Communication based on Mobile Technology for the Safety of Navigation: LTE-Maritime focusing on the Cell Planning and its Verification

  • Shim, Woo-Seong;Kim, Bu-Young;Park, Chan-Yong;Lee, Byeong-Hyeok
    • Journal of Navigation and Port Research
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    • v.45 no.5
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    • pp.231-237
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    • 2021
  • Enhancing the performance of maritime wireless communication has been highlighted by the issue of cell planning in the sea area because of lack of an appropriate Propagation Loss Model (PLM). To resolve the cell planning issue in vast sea areas, it was essential to develop the (PLM) matching the intended sea area. However, there were considerable gaps between the prediction of legacy PLMs and field measurement in propagation loss and there was a need to develop the adjusted PLM (A-PLM). Therefore, cell planning was performed on this adjusted model, including modification of the base station's location, altitude, and antenna azimuth to meet the quality objectives. Furthermore, in order to verify the availability of the cell planning, Communication Service Quality Monitoring System (CS-QMS) was developed in the LTE-Maritime project to collect LTE signal quality information from the onboard equipment at regular intervals and to ensure that the service quality was high enough to satisfy the goals in each designated grid. As a result of verification, the success rate of RSRP was 95.7% for the intensive management zone (IMZ) and 96.4% for the interested zone (IZ), respectively.

Deep Learning based Human Recognition using Integration of GAN and Spatial Domain Techniques

  • Sharath, S;Rangaraju, HG
    • International Journal of Computer Science & Network Security
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    • v.21 no.8
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    • pp.127-136
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    • 2021
  • Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

A Review of Machine Learning Algorithms for Fraud Detection in Credit Card Transaction

  • Lim, Kha Shing;Lee, Lam Hong;Sim, Yee-Wai
    • International Journal of Computer Science & Network Security
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    • v.21 no.9
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    • pp.31-40
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    • 2021
  • The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

Fast Channel Allocation for Ultra-dense D2D-enabled Cellular Network with Interference Constraint in Underlaying Mode

  • Dun, Hui;Ye, Fang;Jiao, Shuhong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.6
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    • pp.2240-2254
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    • 2021
  • We investigate the channel allocation problem in an ultra-dense device-to-device (D2D) enabled cellular network in underlaying mode where multiple D2D users are forced to share the same channel. Two kinds of low complexity solutions, which just require partial channel state information (CSI) exchange, are devised to resolve the combinatorial optimization problem with the quality of service (QoS) guaranteeing. We begin by sorting the cellular users equipment (CUEs) links in sequence in a matric of interference tolerance for ensuring the SINR requirement. Moreover, the interference quota of CUEs is regarded as one kind of communication resource. Multiple D2D candidates compete for the interference quota to establish spectrum sharing links. Then base station calculates the occupation of interference quota by D2D users with partial CSI such as the interference channel gain of D2D users and the channel gain of D2D themselves, and carries out the channel allocation by setting different access priorities distribution. In this paper, we proposed two novel fast matching algorithms utilize partial information rather than global CSI exchanging, which reduce the computation complexity. Numerical results reveal that, our proposed algorithms achieve outstanding performance than the contrast algorithms including Hungarian algorithm in terms of throughput, fairness and access rate. Specifically, the performance of our proposed channel allocation algorithm is more superior in ultra-dense D2D scenarios.