2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)最新文献_第3页

Statistical Analysis of Process Variations on the Delay-Based PUF 基于延迟的PUF过程变化的统计分析

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.104

Songde Hu, Huansheng Ning, Yang Xu, L. Mao, Youzhong Li, Lijun Zhang

引用次数: 1

LSTM Based on the Classification of Emotion about User Evaluation on Shopping Site 基于情感分类的购物网站用户评价LSTM

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.77

Rong Xiao, Xiaohui Cui, Peipei Zhou, Wanfeng Ge

引用次数: 0

Problems and Solutions Encountered in the Measurement of Millimeter Wave Antenna Pattern 毫米波天线方向图测量中遇到的问题及解决方法

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.32

Yonglei Li, Hongmei Tang, Xin Liu, Tiexing Wang

引用次数: 0

A Three Dimension Super-Resolution Algorithm through Neighbor Embedding Based on Weighted Coefficient Values for Internet of Things 基于加权系数值邻域嵌入的物联网三维超分辨算法

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.52

C. Duanmu, Di Zhao, Dingde Jiang, Houbing Song, Jiping Xiong

{"title":"A Three Dimension Super-Resolution Algorithm through Neighbor Embedding Based on Weighted Coefficient Values for Internet of Things","authors":"C. Duanmu, Di Zhao, Dingde Jiang, Houbing Song, Jiping Xiong","doi":"10.1109/IIKI.2016.52","DOIUrl":"https://doi.org/10.1109/IIKI.2016.52","url":null,"abstract":"In the applications of the internet of things (IOT), the nodes in the network usually has power limits and storage space limits. Thus, the images over the IOT are usually low-resolution images. However, the customer usually want high-resolution images. To satisfy these needs, the solution of super-resolution methods comes. In this paper, a new algorithm for super-resolution is proposed, where the three dimensional discrete cosine transform (3D-DCT) is employed to extract the features of the image blocks. Then, the low frequency coefficients in the 3D-DCT are set with large weight values, and the high frequency coefficients are set with relatively small weight values. The 3D-DCT coefficients are then multiplied with the corresponding weights, and a reverse 3D-DCT is carried out. A bi-cubic transform is employed to get a high resolution block. The difference between that block and the actual high resolution block is saved in the training set with the 3D-DCT blocks. A new difference measure is proposed in the online phase to obtain the similarity between a candidate block and the blocks in the training set. After this, several training blocks are selected and the neighbor embedding method is employed to reconstruct the high resolution blocks. To reduce the computational complexity of the algorithm, the revised K-means algorithm is employed. The experimental results show that the proposed algorithm performs much better than the traditional neighbor embedding algorithm and the bi-cubic interpolation algorithm.","PeriodicalId":371106,"journal":{"name":"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114471344","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

KPD Based Signal Preprocessing Algorithm for Pulse Diagnosis 基于KPD的脉冲诊断信号预处理算法

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.75

Zhichao Zhang, Yuan Zhang, W. Jin, A. Kos

{"title":"KPD Based Signal Preprocessing Algorithm for Pulse Diagnosis","authors":"Zhichao Zhang, Yuan Zhang, W. Jin, A. Kos","doi":"10.1109/IIKI.2016.75","DOIUrl":"https://doi.org/10.1109/IIKI.2016.75","url":null,"abstract":"In the traditional Chinese medicine (TCM) wrist pulse diagnosis plays a major role in detecting the health status of an individual. It depends strongly on the doctors' long-term experience as well as on their different inferences. Being subjective and based on long-term experience, pulse detection methods are difficult to standardize. Kim pulse diagnosis (KPD), established by Wei Jin, is an efficient method validated by both traditional Chinese medicine and in recent years also by western medicine. The key step to automatic implementation of KPD, using signal processing and analysis, is the developed KPD signal acquisition device. However, the raw wrist pulse signal acquired from KPD device includes a significant amount of noise. This paper proposes several preprocessing algorithms for pulse diagnosis, that includes wavelet transform and Gaussian filter to remove noise and the iterative sliding window (ISW) algorithm to remove the baseline wander and split the continuous signal into single periods. Experimental results show, that the algorithm for baseline wander removal is efficient and that the segmented signal matches the signal described in KPD.","PeriodicalId":371106,"journal":{"name":"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116095856","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

Improve E-Commerce Recommendation by Classification Tree and Fuzzy Sets 利用分类树和模糊集改进电子商务推荐

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.73

Lianhong Ding, Yanhong Zheng

引用次数: 0

Identification and Selection of Sensors Suitable for Integration into Sport Equipment: Smart Golf Club 适合集成到运动装备中的传感器的识别和选择:智能高尔夫球杆

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.71

A. Kos, A. Umek, S. Tomažič

{"title":"Identification and Selection of Sensors Suitable for Integration into Sport Equipment: Smart Golf Club","authors":"A. Kos, A. Umek, S. Tomažič","doi":"10.1109/IIKI.2016.71","DOIUrl":"https://doi.org/10.1109/IIKI.2016.71","url":null,"abstract":"Smart sport equipment is being increasingly used in highly competitive (professional) sport. By definition, smart equipment employs various sensors for detecting its state and actions. The correct choice of the most appropriate sensor(s) is of paramount importance. When integrated into the equipment, ideal sensors are unobstructive, and do not change the functionality of the equipment. The article focuses on identification and selection of sensors suitable for the integration into a golf club. We used two orthogonally affixed strain gage sensors, a 3-axis accelerometer, and a 3-axis gyroscope. The responses of strain gage sensors are used for measuring golf club flex. They are calibrated and validated in the laboratory environment by the highly accurate optical tracking system Qualisys Track Manager (QTM). Accelerometer and gyroscope are used to measure golf club acceleration and angular speed. Field tests are performed without QTM, only with the sensors affixed to the golf club. The first set of results show that the strain gage sensors complement the inertial sensors. For some golf swing tracking and error detection applications strain gage sensors could be the only type of sensors needed. Our final goal is to be able to acquire and analyze as many parameters of a golf club in real time during the entire swing. Such information would make the identification and selection of the most appropriate sensors to be applicable for a defined task easier.","PeriodicalId":371106,"journal":{"name":"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124693852","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: 4

Research on News Topic-Driven Market Flucatuation and Predication 新闻话题驱动的市场波动与预测研究

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.93

Y. Rao, Xuhui Zhong, Shumin Lu

引用次数: 1

Key Techniques of Cross-Language Medical Term Alignment 跨语言医学术语比对关键技术

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.26

Yuqi Yang, Guangzhi Zhang, R. Bie, Sungjoong Kim, Dongil Shin

引用次数: 0

Differentially Private k-Anonymity: Achieving Query Privacy in Location-Based Services 差分私有k-匿名:实现基于位置服务的查询隐私

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI: 10.1109/IIKI.2016.67

Jinbao Wang, Zhipeng Cai, Chunyu Ai, Donghua Yang, Hong Gao, Xiuzhen Cheng

引用次数: 12