2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)最新文献_第2页

Co-channel interference mitigation on capacity and coverage in cellular systems 蜂窝系统容量和覆盖的同信道干扰缓解

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585224

R. Madhu

{"title":"Co-channel interference mitigation on capacity and coverage in cellular systems","authors":"R. Madhu","doi":"10.1109/ICAIPR.2016.7585224","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585224","url":null,"abstract":"The Coverage and Capacity are the essential parameters that defines the performance of a cellular system. But, these are restricted by the interference in the system. The Co-Channel Interference(CCI) is one of the major sources of interference which exists mainly due to the concept of frequency reuse. The effect of CCI is more in CDMA based systems because the frequency reuse ratio is unity in those systems. In this paper, a new method is proposed to approximate capacity and coverage of a 3G WCDMA under the impact of CCI. The effect of CCI is described in terms of CCI probability. The conditional probability of CCI is analyzed under Rayleigh distributed signals. In this, COST 231 Hata model is described to calculate coverage area. The capacity and coverage are evaluated in terms of different number of Co-Channel interferers, antenna gain ratio, voice activity factor, Eb/No and with various data rates. It is shown that capacity increases with less number of active Co-Channel interferers and with low data rates. The cellular coverage area of WCDMA is improved by increasing the base station antenna height. The scheme proposed in this paper can also be successfully applied to evaluate capacity and coverage in 3GPP LTE and LTE Advanced systems.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132696457","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

On a method of single neural PID feedback compensation control 研究了一种单神经PID反馈补偿控制方法

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585220

Jian Liu

引用次数: 2

Issues on critical objects in mining algorithms 挖掘算法中关键对象问题

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585211

H. Yazdani, H. Kwasnicka

{"title":"Issues on critical objects in mining algorithms","authors":"H. Yazdani, H. Kwasnicka","doi":"10.1109/ICAIPR.2016.7585211","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585211","url":null,"abstract":"Data objects are considered as fundamental keys in learning methods that without the objects the mining algorithms are meaningless. Data objects basically direct the accuracy of the selected algorithm in case if they are extracted from inappropriate groups. Knowing the exact type of data object leads the miner to provide a suitable environment for learning algorithms. Supervised and unsupervised learning methods propose some membership functions that perform with respect to behaviour of each data category to classify data objects and solutions. The paper explores different type of data objects by categorizing them based on their behaviour with respect to learning methods. We also introduce some critical objects that play the main role in each data set. Issues on critical objects in mining algorithms are fully discussed in this paper. The accuracy and behaviour of these critical objects are compared by running fuzzy, probabilistic, and possibilistic algorithms on some data sets presented in this paper. The results prove that some methods are able to provide a suitable environment for critical objects and some are not. The comparison results also show that most of the learning methods have difficulties dealing with critical objects. Lack of ability to deal with these objects may cause irreparable consequences.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"50 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120853286","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: 7

Revocation basis and proofs access control for cloud storage multi-authority systems 云存储多授权系统的撤销基础和证明访问控制

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585223

Khaled Riad

{"title":"Revocation basis and proofs access control for cloud storage multi-authority systems","authors":"Khaled Riad","doi":"10.1109/ICAIPR.2016.7585223","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585223","url":null,"abstract":"Multi-Authority Ciphertext-Policy Attribute-Based Encryption (MA-CP-ABE) is a rising cryptographic primitive for implementing fine-grained attribute-based access control on the outsourced data in cloud storage. However, most of the previous multi-authority attribute-based systems area unit either proved to be secure in a very weak model or lack of potency in user revocation. In this paper, we have introduced the formal definition of the attributes' trust. Also, a new Revocation Basis and Proofs Access Control (RB-PAC) model for cloud storage multi-authority systems has been proposed. Our RB-PAC model ensures secure resource sharing among potential untrusted tenants, supports different access permissions to the same user at the same session, and effectively satisfies both the backward and forward secrecy security requirements. Also, RB-PAC is proven secure against the users' collusion attack. Finally, The experimental results have indicated through the trust dynamics that the trust level for each user is decaying over time. The decryption overhead is largely eliminated and not related to the number of system authorities. Also, a low overhead and short ciphertext update time at different numbers of revoked attributes and revoked users has been achieved.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116059166","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: 3

New similarity functions 新的相似度函数

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585210

H. Yazdani, D. Ortiz-Arroyo, H. Kwasnicka

{"title":"New similarity functions","authors":"H. Yazdani, D. Ortiz-Arroyo, H. Kwasnicka","doi":"10.1109/ICAIPR.2016.7585210","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585210","url":null,"abstract":"In data science, there are some parameters that affect the accuracy of selected algorithms, regardless of their type. Type of data objects, membership assignments, and distance or similarity functions are the most important parameters that provide or not a proper environment for learning algorithms. The paper evaluates similarity functions as fundamental keys for membership assignments. The issues on conventional similarity functions are discussed in this paper. The paper introduces Weighted Feature Distance (WFD), and Prioritized Weighted Feature Distance (PWFD) to cover diversity in feature spaces. Most of the conventional distance functions compare data objects on vector space where any dominant feature may massively skew the final results. WFD functions perform better in supervised and unsupervised methods by comparing data objects on their feature spaces in addition to covering similarity on vector space. Prioritized Weighted Feature Distance (PWFD) works as same as WFD with ability to give priorities to desirable features. The accuracy of proposed functions are compared with other similarity functions on some data sets. Promising results show that the proposed functions work better than the other methods presented in this literature.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130643134","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: 13

Vulnerability of advanced encryption standard algorithm to differential power analysis attacks implemented on ATmega-128 microcontroller 基于ATmega-128微控制器的高级加密标准算法易受差分功耗分析攻击

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585214

K. Mpalane, N. Gasela, B. M. Esiefarienrhe, H. D. Tsague

{"title":"Vulnerability of advanced encryption standard algorithm to differential power analysis attacks implemented on ATmega-128 microcontroller","authors":"K. Mpalane, N. Gasela, B. M. Esiefarienrhe, H. D. Tsague","doi":"10.1109/ICAIPR.2016.7585214","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585214","url":null,"abstract":"A wide variety of cryptographic embedded devices including smartcards, ASICs and FPGAs must be secure against breaking in. However, these devices are vulnerable to side channel attacks. A side channel attack uses physical attributes such as differences in the power consumption measured from the physical implementation of the cryptosystem while it is performing cryptographic operations to determine the secret key of the device. This paper investigates the vulnerability of 128-bits advanced encryption standard(AES) cryptographic algorithm implementation in a microcontroller crypto-device against differential power analysis (DPA) attacks. ChipWhisperer capture hardware Rev2 tool was used to collect 1000 power traces for DPA. We observed and measured the behaviour of the power consumption of the microcontroller while it was encrypting 1000 randomly generated plaintexts using the same secret key throughout. Our attack was successful in revealing all the 16 bytes (128-bits) of the secret key and the results demonstrated that the AES implementation can be broken using 1000 encryption operations.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133519473","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

A machine learning approach for predicting bank credit worthiness 预测银行信用价值的机器学习方法

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585216

Regina Esi Turkson, E. Baagyere, Gideon Evans Wenya

{"title":"A machine learning approach for predicting bank credit worthiness","authors":"Regina Esi Turkson, E. Baagyere, Gideon Evans Wenya","doi":"10.1109/ICAIPR.2016.7585216","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585216","url":null,"abstract":"Machine learning is an emerging technique for building analytic models for machines to \"learn\" from data and be able to do predictive analysis. The ability of machines to \"learn\" and do predictive analysis is very important in this era of big data and it has a wide range of application areas. For instance, banks and financial institutions are sometimes faced with the challenge of what risk factors to consider when advancing credit/loans to customers. For several features/attributes of the customers are normally taken into consideration, but most of these features have little predictive effect on the credit worthiness or otherwise of the customer. Furthermore, a robust and effective automated bank credit risk score that can aid in the prediction of customer credit worthiness very accurately is still a major challenge facing many banks. In this paper, we examine a real bank credit data and conduct several machine learning algorithms on the data for comparative analysis and to choose which algorithms are the best fit for learning bank credit data. The algorithms gave over 80% accuracy in prediction. Furthermore, the most important features that determine whether a customer will default or otherwise in paying his/her credit the next month are extracted from a total of 23 features. We then applied these most important features on some selected machine learning algorithms and compare their predictive accuracy with the other algorithms that used all the 23 features. The results show no significant di erence, signifying that these features can accurately determine the credit worthiness of the customers. Finally, we formulate a predictive model using the most important features to predict the credit worthiness of a given customer.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123150979","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: 33

Prediction of production line performance using neural networks 基于神经网络的生产线性能预测

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585212

Dominika Janíková, P. Bezák

引用次数: 3

Decision support system based on petri net for a police vehicle command and control system 基于petri网的警车指挥控制系统决策支持系统

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585213

Sasan Harifi, B. Nakhjavanlo

引用次数: 1

A comparison between person re-identification approaches 人物再识别方法的比较

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585208

Bassem Hadjkacem, W. Ayedi, M. Abid

引用次数: 6