• Title/Summary/Keyword: feature strength information

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Speed-up of Image Matching Using Feature Strength Information (특징 강도 정보를 이용한 영상 정합 속도 향상)

  • Kim, Tae-Woo
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.13 no.6
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    • pp.63-69
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    • 2013
  • A feature-based image recognition method, using features of an object, can be performed faster than a template matching technique. Invariant feature-based panoramic image generation, an application of image recognition, requires large amount of time to match features between two images. This paper proposes a speed-up method of feature matching using feature strength information. Our algorithm extracts features in images, computes their feature strength information, and selects strong features points which are used to match the selected features. The strong features can be referred to as meaningful ones than the weak features. In the experiments, it was shown that our method speeded up over 40% of processing time than the technique without using feature strength information.

Wireless Channel Identification Algorithm Based on Feature Extraction and BP Neural Network

  • Li, Dengao;Wu, Gang;Zhao, Jumin;Niu, Wenhui;Liu, Qi
    • Journal of Information Processing Systems
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    • v.13 no.1
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    • pp.141-151
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    • 2017
  • Effective identification of wireless channel in different scenarios or regions can solve the problems of multipath interference in process of wireless communication. In this paper, different characteristics of wireless channel are extracted based on the arrival time and received signal strength, such as the number of multipath, time delay and delay spread, to establish the feature vector set of wireless channel which is used to train backpropagation (BP) neural network to identify different wireless channels. Experimental results show that the proposed algorithm can accurately identify different wireless channels, and the accuracy can reach 97.59%.

Ultrasonic Pattern Recognition of Welding Defects Using the Chaotic Feature Extraction (카오스 특징 추출에 의한 용접 결함의 초음파 형상 인식)

  • Lee, Won;Yoon, In-Sik;Lee, Byung-Chae
    • Journal of the Korean Society for Precision Engineering
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    • v.15 no.6
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    • pp.167-174
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    • 1998
  • The ultrasonic test is recognized for its significance as a non-destructive testing method to detect volume defects such as porosity and incomplete penetration which reduce strength in the weld zone. This paper illustrates the defect detection in the weld zone of ferritic carbon steel using ultrasonic wave and the evaluation of pattern recognition by chaotic feature extraction using time series signal of detected defects as data. Shown in the time series data were that the time delay was 4 and the embedding dimension was 6 which indicate the geometric dimension of the subject system and the extent of information correlation. Based on fractal dimension and lyapunov exponent in quantitative chaotic feature extraction, feature value of 2.15, 0.47 is presented for porosity and 2.24, 0.51 for incomplete penetration The precision rate of the pattern recognition is enhanced with these values on the total waveform of defect signal in the weld zone. Therefore, we think that the ultrasonic pattern recognition method of weld zone defects of ferritic carbon steel by ultrasonic-chaotic feature extraction proposed in this paper can boost precision rate further than the existing method applying only partial waveform.

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Speaker Verification System Using Support Vector Machine with Genetic Algorithms (유전자 알고리즘을 결합한 Support Vector Machine의 화자인증에서의 성능분석)

  • 최우용;이경희;반성범
    • Proceedings of the IEEK Conference
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    • 2003.11a
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    • pp.557-560
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    • 2003
  • Voice is one of the promising biometrics because it is one of the most convenient ways human would distinguish someone from others. The target of speaker verification is to divide the client from imposters. Support Vector Machine(SVM) is in the limelight as a binary classifier, so it can work well in speaker verification. In this paper, we combined SVM with genetic algorithm(GA) to reduce the dimensionality of input feature. Experiments were conducted with Korean connected digit database using different feature dimensions. The verification accuracy of SVM with GA is slightly lower than that of SVM, but the proposed algorithm has greater strength in the memory limited systems.

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Indoor Positioning Using the WLAN-based Wavelet and Neural Network (WLAN 기반의 웨이블릿과 신경망을 이용한 위치인식 방법)

  • Kim, Jong-Bae
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.45 no.5
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    • pp.38-47
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    • 2008
  • The most commonly used location recognition system is the GPS-based approach. However, the GPS is inefficient for an indoor or urban area where high buildings shield the satellite signals. To overcome this problem, this paper propose the indoor positioning method using wavelet and neural network. The basic idea of proposed method is estimated the location using the received signal strength from wireless APs installed in the indoor environment. Because of the received signal strength of wireless radio signal is fluctuated by the environment factors, a feature that is strength of signal noise and error and express the time and frequency domain is need. Therefore, this paper is used the wavelet coefficient as the feature. And the neural network is used for estimate the location. The experiment results indicate 94.6% an location recognition rate.

Prosodic Annotation in a Thai Text-to-speech System

  • Potisuk, Siripong
    • Proceedings of the Korean Society for Language and Information Conference
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    • 2007.11a
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    • pp.405-414
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    • 2007
  • This paper describes a preliminary work on prosody modeling aspect of a text-to-speech system for Thai. Specifically, the model is designed to predict symbolic markers from text (i.e., prosodic phrase boundaries, accent, and intonation boundaries), and then using these markers to generate pitch, intensity, and durational patterns for the synthesis module of the system. In this paper, a novel method for annotating the prosodic structure of Thai sentences based on dependency representation of syntax is presented. The goal of the annotation process is to predict from text the rhythm of the input sentence when spoken according to its intended meaning. The encoding of the prosodic structure is established by minimizing speech disrhythmy while maintaining the congruency with syntax. That is, each word in the sentence is assigned a prosodic feature called strength dynamic which is based on the dependency representation of syntax. The strength dynamics assigned are then used to obtain rhythmic groupings in terms of a phonological unit called foot. Finally, the foot structure is used to predict the durational pattern of the input sentence. The aforementioned process has been tested on a set of ambiguous sentences, which represents various structural ambiguities involving five types of compounds in Thai.

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Porting Window CE Operating System to Arm based board device

  • An, Byung-Chan;Ham, Woon-Chul
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.2159-2163
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    • 2003
  • Hand carried computing machinery and tools have been developed into an embedded system which the small footprint operating system is contained internally. Windows CE which is one of imbedded operating system is a lightweight, multithreaded operating system with an optional graphical user interface. Its strength lies in its small size, its Win32 subset API, and its multiplatform support. Therefore we choose to port this OS on Arm based board that is provided high performance, low cost, and low power consumption. In this paper, we describe the architecture of ARM based board, the feature of Windows CE, techniques and steps involved in this porting process.

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Research Trends Analysis of Machine Learning and Deep Learning: Focused on the Topic Modeling (머신러닝 및 딥러닝 연구동향 분석: 토픽모델링을 중심으로)

  • Kim, Chang-Sik;Kim, Namgyu;Kwahk, Kee-Young
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.15 no.2
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    • pp.19-28
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    • 2019
  • The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', 'deep learning', and 'artificial neural network' in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.

Privacy-Preserving NFC-Based Authentication Protocol for Mobile Payment System

  • Ali M. Allam
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.5
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    • pp.1471-1483
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    • 2023
  • One of the fastest-growing mobile services accessible today is mobile payments. For the safety of this service, the Near Field Communication (NFC) technology is used. However, NFC standard protocol has prioritized transmission rate over authentication feature due to the proximity of communicated devices. Unfortunately, an adversary can exploit this vulnerability with an antenna that can eavesdrop or alter the exchanged messages between NFC-enabled devices. Many researchers have proposed authentication methods for NFC connections to mitigate this challenge. However, the security and privacy of payment transactions remain insufficient. We offer a privacy-preserving, anonymity-based, safe, and efficient authentication protocol to protect users from tracking and replay attacks to guarantee secure transactions. To improve transaction security and, more importantly, to make our protocol lightweight while ensuring privacy, the proposed protocol employs a secure offline session key generation mechanism. Formal security verification is performed to assess the proposed protocol's security strength. When comparing the performance of current protocols, the suggested protocol outperforms the others.

Object Location Sensing using Signal Pattern Matching Methods (신호 패턴 매칭 방법을 이용한 이동체 위치 인식)

  • Byun, Yung-Cheol;Park, Sang-Yeol
    • Journal of Korea Multimedia Society
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    • v.10 no.4
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    • pp.548-558
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    • 2007
  • This paper presents a method of location sensing of mobile objects using RF devices. By analyzing signal strengths between a certain number of fixed RF devices and a moving RF device, we can recognize the location of a moving object in real time. Firstly, signal strength values between RF devices are gathered, and then the values are normalized and constructed as a model feature vector for specific location. A number of model patterns are acquired and registered for all of the location which we want to recognize. For location sensing, signal strength information for an arbitrary moving RF device is acquired and compared with model feature vectors registered previously. In this case, distance value is calculated and the moving RF device is classified as one of the known model patterns. Experimental results show that our methods have performed the location sensing successfully with 100% rate of recognition when the number of fixed RF devices is 10 or more than 12. In terms of cost and applicability, experimental results seem to be very encouraging.

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