2015 Signal Processing and Intelligent Systems Conference (SPIS)最新文献

An improved DV-Hop localization algorithm in wireless sensor networks 一种改进的无线传感器网络DV-Hop定位算法

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422331

Mohaddeseh Peyvandi, A. Pouyan

{"title":"An improved DV-Hop localization algorithm in wireless sensor networks","authors":"Mohaddeseh Peyvandi, A. Pouyan","doi":"10.1109/SPIS.2015.7422331","DOIUrl":"https://doi.org/10.1109/SPIS.2015.7422331","url":null,"abstract":"Localization as a fundamental issue has a challenge in wireless sensor networks (WSNs). Many approaches have been proposed to solve the inaccurate node localization. Among range-free algorithms, DV-Hop (Distance Vector-hop) is a well-known localization algorithm that utilizes of hop-distance estimation to locate sensor nodes. This has lead positioning accuracy is limited. In this paper, an improved DV-HOP algorithm based on the hop-size correction and localization optimization is put forward. Firstly, based on difference of actual and estimated distance between reference nodes an effective hop-size is calculated for whole network; secondly, a correction value is added to the hops between unknown nodes and reference nodes while received signal strength indicator (RSSI) value is used to correct the distance of single hop. Finally, the Levenberg-Marquardt algorithm is applied to estimate an optimize position for each sensors. In evaluation step, various factors that affect the localization accuracy of the DV-Hop are investigated. Simulation results show that the proposed algorithm has been significantly improved compared to the basic DV-Hop and some existing improved algorithms.","PeriodicalId":424434,"journal":{"name":"2015 Signal Processing and Intelligent Systems Conference (SPIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115448426","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

Toward a robust and secure echo steganography method based on parameters hopping 基于参数跳变的鲁棒安全回波隐写方法

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422329

Hamzeh Ghasemzadeh, M. Kayvanrad

引用次数: 15

Brain extraction: A region based histogram analysis strategy 脑提取:一种基于区域的直方图分析策略

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422305

H. Khastavaneh, H. Ebrahimpour-Komleh

{"title":"Brain extraction: A region based histogram analysis strategy","authors":"H. Khastavaneh, H. Ebrahimpour-Komleh","doi":"10.1109/SPIS.2015.7422305","DOIUrl":"https://doi.org/10.1109/SPIS.2015.7422305","url":null,"abstract":"Brain extraction is the task of removing non-brain tissues from brain magnetic resonance images. Brain extraction is a preprocessing step in many applications related to the brain image analysis. Accurate extraction of brain tissue is a laborious task. So, automatic extraction of it is a need in many applications. In this study we propose an automatic region based brain extraction method. In this method histogram of each region is independently analyzed and parameters relating to each tissue type is estimated by employing expectation-maximization algorithm. The estimated parameters of each tissue type including its mean and variance are used to determine tissues of interests. In this study tissues of interest are gray matter and white mater. Eventually a connected component analysis leads to select largest connected components of tissues of interest as brain mask. The proposed method is tested on BrainWeb dataset. Jaccard similarity index (J), Dice similarity coefficient (DSC), Sensitivity (Sen), and Specificity (Spec) are used to measure performance of the proposed method. The results are compared to three popular brain extraction methods namely hybrid watershed algorithm (HWA), brain extraction tools (BET), and brain surface extractor (BSE). The proposed method outperforms mentioned popular methods.","PeriodicalId":424434,"journal":{"name":"2015 Signal Processing and Intelligent Systems Conference (SPIS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124139088","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

Traffic sign recognition using an extended bag-of-features model with spatial histogram 基于空间直方图扩展特征袋模型的交通标志识别

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422338

Mahsa Mirabdollahi Shams, H. Kaveh, R. Safabakhsh

引用次数: 4

A new method for traffic density estimation based on topic model 基于主题模型的交通密度估计新方法

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422323

Razie Kaviani, P. Ahmadi, I. Gholampour

引用次数: 7

Dynamic prediction scheduling for virtual machine placement via ant colony optimization 基于蚁群优化的虚拟机布局动态预测调度

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422321

Milad Seddigh, H. Taheri, Saeed Sharifian

{"title":"Dynamic prediction scheduling for virtual machine placement via ant colony optimization","authors":"Milad Seddigh, H. Taheri, Saeed Sharifian","doi":"10.1109/SPIS.2015.7422321","DOIUrl":"https://doi.org/10.1109/SPIS.2015.7422321","url":null,"abstract":"Virtual machine (VM) scheduling with load balancing in cloud computing aims to allocate VMs to suitable physical machines (PM) and balance the resource usage among all of the PMs. Correct scheduling of cloud hosts is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. In this regard the use of dynamic forecast of resource usage in each PM can improve the VM scheduling problem. This paper combines ant colony optimization (ACO) and VM dynamic forecast scheduling (VM_DFS), called virtual machine dynamic prediction scheduling via ant colony optimization (VMDPS-ACO), to solve the VM scheduling problem. In this algorithm through analysis of historical memory consumption in each PM, future memory consumption forecast of VMs on that PM and the efficient allocation of VMs on the cloud infrastructure is performed. We experimented the proposed algorithm using Matlab. The performance of the proposed algorithm is compared with VM_DFS. VM_DFS algorithm exploits first fit decreasing (FFD) scheme using corresponding types (i.e. queuing the list of VMs increasingly, decreasingly or randomly) to schedule VMs and assign them to suitable PMs. We experimented the proposed algorithm in both homogeneous and heterogeneous mode. The results indicate, VMDPS-ACO produces lower resource wastage than VM_DFS in both homogenous and heterogeneous modes and better load balancing among PMs.","PeriodicalId":424434,"journal":{"name":"2015 Signal Processing and Intelligent Systems Conference (SPIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126596798","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: 28

Design an intelligent curve to reduce the accident rate 设计智能曲线，降低事故率

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422337

Negin Massoudian, M. Eshghi

引用次数: 2

User-friendly visual secret sharing based on random grids 基于随机网格的用户友好的可视化秘密共享

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422312

S. Paknahad, S. A. Hosseini, Mahdi R. Alaghband

引用次数: 0

A novel compressed sensing DOA estimation using difference set codes 一种基于差分集码的压缩感知DOA估计方法

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422330

Iman Taghavi, M. Sabahi, F. Parvaresh, M. Mivehchy

引用次数: 0

License plate recognition based on edge histogram analysis and classifier ensemble 基于边缘直方图分析和分类器集成的车牌识别

2015 Signal Processing and Intelligent Systems Conference (SPIS) Pub Date : 2015-12-01 DOI: 10.1109/SPIS.2015.7422310

M. Nejati, A. Majidi, Morteza Jalalat

{"title":"License plate recognition based on edge histogram analysis and classifier ensemble","authors":"M. Nejati, A. Majidi, Morteza Jalalat","doi":"10.1109/SPIS.2015.7422310","DOIUrl":"https://doi.org/10.1109/SPIS.2015.7422310","url":null,"abstract":"In this paper, a new approach for Iranian vehicle license plate recognition (LPR) is proposed. The proposed algorithm contains four main steps including plate localization, segmentation, recognition, and fusion of multiple recognition results. The license plate localization is begun with some preprocessing for down-sampling, denoising and histogram equalization. Then, histogram of vertical edges is used for detection of candidate lines expected to contain the license plate. In this step, we design a filter in order to reduce the number of false line candidates. The candidate plates are then extracted using vertical projection histogram of edges and aspect ratio characteristic. The binary image of these candidates obtained by locally adaptive thresholding is transmitted to the segmentation and recognition modules. Our recognition method is accomplished using a classifier ensemble with mixture of experts architecture. Using a feedback from the recognition result of candidate plates, the true candidate is detected. To improve the recognition accuracy and robustness, we apply the proposed LPR on three consecutive frames of a vehicle captured by different exposure times and then combine their recognition outputs. The experimental results in practical situations of day and night show that the proposed approach leads to 95.39% accuracy in plate localization and 92.45% overall accuracy after recognition.","PeriodicalId":424434,"journal":{"name":"2015 Signal Processing and Intelligent Systems Conference (SPIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122250098","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: 16