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International Conference on Critical Infrastructure Protection最新文献

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Analysis of affecting technology adoption factors in online transportation reservation for smartphone application: case study: PT. GoJek Indonesia 智能手机应用中影响在线交通预订的技术采用因素分析:以印尼PT. GoJek为例
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290435
A. Suzianti, Ratna Herawati, Yogi Septiandi
{"title":"Analysis of affecting technology adoption factors in online transportation reservation for smartphone application: case study: PT. GoJek Indonesia","authors":"A. Suzianti, Ratna Herawati, Yogi Septiandi","doi":"10.1145/3290420.3290435","DOIUrl":"https://doi.org/10.1145/3290420.3290435","url":null,"abstract":"The new service of online transportation reservation is currently popular, encouraging other people to join as a driver and as a user. However, there are still many difficulties in using the applications from both the driver's side and the user's side. A related study of users and driver's perception about technology adoption is needed to develop transportation reservation applications. This research focuses on case study of PT. Gojek Indonesia. Analysis of factors related to users and drivers' technology adoption of transportation reservation applications will be done using the integrated model Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The analysis will be conducted and the data will be processed using the method of Structural Equation Modelling (SEM). The results showed low attitude towards behavior, yet high perceived usefulness from the driver's side so Gojek is expected to improve the quality of its application to enhance the positive impression from the driver's side. From the user side, the attitude towards behavior and perceived usefulness has been rated high enough so that Gojek is expected to maintain their current position by providing some innovation.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116095099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An improved AOR-based precoding for massive MIMO systems 一种改进的基于or的大规模MIMO系统预编码
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290427
Jiayu Wu, Yanjun Hu, Yi Wang
{"title":"An improved AOR-based precoding for massive MIMO systems","authors":"Jiayu Wu, Yanjun Hu, Yi Wang","doi":"10.1145/3290420.3290427","DOIUrl":"https://doi.org/10.1145/3290420.3290427","url":null,"abstract":"Compared with traditional multiple-input-multiple-output (MIMO) systems, the number of base station (BS) antennas increased in massive MIMO systems. However, due to the huge number of antennas, linear precoding schemes are able to achieve the near-optimal performance. Conventional linear precoding schemes such as regularized zero forcing (RZF) precoding need to calculate the matrix inversion of large size which leads to high computational complexity. Although utilizing iterative algorithm approximate instead of matrix inversion can reduce the complexity, it leads to slow convergence or general bit error rate (BER) performance. To solve this problem, we proposes a linear precoding scheme based on the accelerated over relaxation (AOR) method. Moreover, we propose a simple way to choose the optimal accelerate factor so that it is only related to the system parameters and more suitable for practical application. Simulation results prove that the improved AOR-based precoding could convergence faster and had better performance of bit error rate (BER).","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114728180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Research and implementation of network location algorithm based on LoRa system 基于LoRa系统的网络定位算法的研究与实现
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290455
Zhengyi Cui, Xiaodong Cheng, Ning Ding, Wenhua Wang, Xiaofang Wang, M. Li
{"title":"Research and implementation of network location algorithm based on LoRa system","authors":"Zhengyi Cui, Xiaodong Cheng, Ning Ding, Wenhua Wang, Xiaofang Wang, M. Li","doi":"10.1145/3290420.3290455","DOIUrl":"https://doi.org/10.1145/3290420.3290455","url":null,"abstract":"This paper analyzes the application status of wireless positioning technology in wireless sensor network, studies the commonly used RSSI positioning algorithm, analyzes the defects therein, and proposes a new positioning algorithm based on Euclidean distance. Finally, taking Lora module as the core, the wireless sensor network was built, the software and hardware were designed and realized, and the feasibility and accuracy of the method was verified through experiments.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127786777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An improved dual threshold DMM cooperative spectrum sensing algorithm 一种改进的双阈值DMM协同频谱感知算法
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290439
Weiting Gao, Haifeng Zhu, Guobing Cheng, Fei Ma, Weilun Liu
{"title":"An improved dual threshold DMM cooperative spectrum sensing algorithm","authors":"Weiting Gao, Haifeng Zhu, Guobing Cheng, Fei Ma, Weilun Liu","doi":"10.1145/3290420.3290439","DOIUrl":"https://doi.org/10.1145/3290420.3290439","url":null,"abstract":"For problems of eigenvalue-based dual thresholds spectrum sensing algorithms overlooking the reliability difference between second users (SU) and its high expense of decisions, a dual threshold DMM cooperative spectrum sensing algorithm (DT-CDMM) based on credibility was proposed to improve the sensing performance. Based on difference between maximum and minimum eigenvaluealgorithm (DMM), a dual thresholds DMM spectrum sensing algorithm based on limiting eigenvalue distribution was used as SU's local sensing, a triggered mechanism combined with soft and hard decisions was established to cut the system expenses, the final decision was obtained via the weighting of SU's sensing performance and local credibility, and an self-adaption compensation for hard decisions was applied to proposed algorithm. Theory analysis and simulations shown the DT-CDMM has performance improvement over classical dual thresholds algorithms with noise uncertainty.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126594707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Uplink-aided downlink channel estimation in FDD massive MIMO by variational Bayesian inference 基于变分贝叶斯推理的FDD海量MIMO上行辅助下行信道估计
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290448
Wei Lu, Yongliang Wang, Xiaoqiang Hua, Wei Zhang, Shixin Peng, Liang Zhong
{"title":"Uplink-aided downlink channel estimation in FDD massive MIMO by variational Bayesian inference","authors":"Wei Lu, Yongliang Wang, Xiaoqiang Hua, Wei Zhang, Shixin Peng, Liang Zhong","doi":"10.1145/3290420.3290448","DOIUrl":"https://doi.org/10.1145/3290420.3290448","url":null,"abstract":"In this paper, we discuss the downlink channel estimation in frequency division duplex (FDD) massive MIMO system. Based on the angular reciprocity between uplink and downlink, we combined the uplink support prior information into the downlink channel estimation. A downlink channel estimation method based on variational Bayesian inference(VBI) is proposed, which is by taking the support prior information into consideration. Meanwhile the VBI is discussed for complex number in our system model, and the structural sparsity is utilized in the Bayesian inference. The Bayesian Cramer-Rao bound for the channel estimation MSE is also given out. Compared with Bayesian compressed sensing and other algorithms, the proposed algorithm achieves much better performance in terms of channel estimation accuracy by simulations.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116735233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Guitar chord recognition based on finger patterns with deep learning 基于手指模式的深度学习吉他和弦识别
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290422
Takumi Ooaku, Tran Duy Linh, Masayuki Arai, Tsukasa Maekawa, K. Mizutani
{"title":"Guitar chord recognition based on finger patterns with deep learning","authors":"Takumi Ooaku, Tran Duy Linh, Masayuki Arai, Tsukasa Maekawa, K. Mizutani","doi":"10.1145/3290420.3290422","DOIUrl":"https://doi.org/10.1145/3290420.3290422","url":null,"abstract":"Many guitar players use video contents such as Youtube to practice. If the content contains noise or background sounds, then the player must watch the videos repeatedly, which is very troublesome. In order to solve this problem, we attempt to build a system that can recognize the finger patterns of guitar players in video and can automatically generate a corresponding musical score. The present paper introduces a method to recognize finger patterns with deep learning. Experimental results reveal that a three-chord classifier can achieve a recognition rate of approximately 90% and a five-chord classifier can achieve a recognition rate of approximately 70%.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134426708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
A SVD-based 2DOMP algorithm for compressed image sensing 基于svd的压缩图像感知2DOMP算法
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290457
Cheng Zhang, Qianwen Chen, Meiqin Wang, D. Wang, Sui Wei
{"title":"A SVD-based 2DOMP algorithm for compressed image sensing","authors":"Cheng Zhang, Qianwen Chen, Meiqin Wang, D. Wang, Sui Wei","doi":"10.1145/3290420.3290457","DOIUrl":"https://doi.org/10.1145/3290420.3290457","url":null,"abstract":"We use the singular value decomposition of the separable measurement matrices to obtain the optimized separable reconstruction matrices and optimize the measurements. A two-dimensional orthogonal matching pursuit optimization algorithm based on singular value decomposition is proposed. Numerical experiments demonstrate that our proposed 2DOMP-SVD algorithm can significantly improve reconstruction quality and signal to noise ratio. Moreover, separable imaging operator arise naturally in many optical implementations and can satisfy the requirements for both the measurement matrix and the reconstruction matrix individually. And this design is suitable for general separable linear system.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122701297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
CW-kNN: an efficient kNN-based model for imbalanced dataset classification CW-kNN:一种高效的基于knn的不平衡数据集分类模型
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290431
Yi Xiang, Zhong Cao, Shaowen Yao, Jing He
{"title":"CW-kNN: an efficient kNN-based model for imbalanced dataset classification","authors":"Yi Xiang, Zhong Cao, Shaowen Yao, Jing He","doi":"10.1145/3290420.3290431","DOIUrl":"https://doi.org/10.1145/3290420.3290431","url":null,"abstract":"K nearest neighbor (kNN) method is a popular classification method in data mining because of its simple implementation and significant classification performance. However, kNN do not scale well to big datasets. In this paper, CLUKER, a novel kNN regression method based on hierarchical clustering, is proposed. CLUKER uses hierarchical clustering to divide the original dataset into several parts, effectively reducing the query scope of kNN. Moreover, in order to improve kNN's ability to handle imbalanced datasets, this paper proposes a novel weighting method based on local data distribution, called LD-Weighting method. In the end, having integrated the two algorithms, this paper proposes an efficient kNN-based model for imbalanced dataset classification called CW-kNN. The experimental results show that the proposed methods perform well on different datasets.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130893933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Image feature fusion and its application based on trace transform and improved GLBP 基于迹变换和改进GLBP的图像特征融合及其应用
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290453
Tao Shen, Niande Jiang, Guoyun Zhong
{"title":"Image feature fusion and its application based on trace transform and improved GLBP","authors":"Tao Shen, Niande Jiang, Guoyun Zhong","doi":"10.1145/3290420.3290453","DOIUrl":"https://doi.org/10.1145/3290420.3290453","url":null,"abstract":"In order to solve the problem that the features extracted by the trace transform are not good enough for the description of face images, a target recognition method based on the image transform and the improved gradient local binary pattern (GLBP) is proposed. This method combines the trace transform and the improved gradient local binary mode. By selecting sampling points on the trace line and carrying out GLBP coding, the GLBP texture feature information that can describe the whole trace line is extracted. Classification experiments on ORL face database show that under the circumstances of less training samples, the target recognition based on trace transform and GLBP method to extract the texture characteristics, its recognition ability has obvious improvement than trace transform, within a smaller fluctuation range, better stability and better resolution for texture image.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114671564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
3D anisotropie convolutional neural network with step transfer learning for liver segmentation 基于步进迁移学习的三维各向异性卷积神经网络肝脏分割
International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290461
Xiaoying Pan, Zhe Zhang, Yuping Zhang
{"title":"3D anisotropie convolutional neural network with step transfer learning for liver segmentation","authors":"Xiaoying Pan, Zhe Zhang, Yuping Zhang","doi":"10.1145/3290420.3290461","DOIUrl":"https://doi.org/10.1145/3290420.3290461","url":null,"abstract":"Automatic liver segmentation on computed tomography (CT) slices plays an important role in current clinical practice for liver cancer supporting diagnosis. While manual segmentation is accurate and precise, it is time-consuming and tedious. Otherwise, automatic segmenting liver from raw CT images suffers from insufficient GPU memory and poor generalization. In this paper, we propose an algorithm to automatically segment liver from CT abdomen images. Frist and foremost, to enlarge receptive field of Convolutional Neural Network (CNN), we apply Atrous layer and Atrous Spatial Pyramid Pooling (ASPP) to our image segmentation network. Furthermore, because it is difficult to train a deep CNN image segmentation network with the is relatively small amount of 3D volumes, we propose step transfer learning technique to boost model performance. Finally, due to the within-slice resolution is much higher than between-slice in CT images, we propose a 3D anisotropic deep CNN network to segment raw CT image from axial, coronal and sagittal axis. Our experiments show that the proposed method achieves Dice scores over 95% on LiST dataset, which is comparable to the state-of-the-art performance. Experimental results demonstrate that the presented model on liver segmentation task is powerful.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114435515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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