2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)最新文献

{"title":"Energy Efficiency Proposal for IoT Call Admission Control in 5G Network","authors":"A. Slalmi, Hatim Kharraz, Rachid Saadane, Chaibi Hasna, A. Chehri, Gwanggil Jeon","doi":"10.1109/SITIS.2019.00070","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00070","url":null,"abstract":"Internet of Things (IoT) started with the idea of connecting both wireless and wired sensors to the Internet network that can be found in homes, offices, or everywhere. Then, its major contribution was raised by RFID (Radio Frequency Identification) and electronic tags. The IoT makes it possible to connect everything that is connectable, from various objects to \"Smart Dust.\" The concept is simple. However, there are many problems, because \"things\" are not usually sophisticated enough to handle applications-related communications and processing. Some mobile networks (4G LTE) are used, such as LTE-M (LTE for Machine Type Communication) and NB-IoT (Narrowband IoT), which are LPWA technologies (Low Power Wide Area) standardized by 3GPP. There are also other used LPWA technologies such as LoRa, Sigfox. Nowadays, the number of connected objects becomes to increases very quickly as well as bit-rates and energy consumption. For this, the 5G will provide solutions to this problem, although it manages an Ultra-Dense Network (UDN) requiring a lot of energy. In this paper, we suggest a Call Admission Control (CAC) modeling algorithm for IoT in a New Radio Access (NR 5G), essentially based on minimal energy consumption.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127645313","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}

{"title":"Autoencoder Based Dimensionality Reduction of Feature Vectors for Object Recognition","authors":"Reyhan Kevser Keser, B. U. Töreyin","doi":"10.1109/SITIS.2019.00097","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00097","url":null,"abstract":"Object recognition can be performed with high accuracy thanks to the robust feature descriptors defining the significant areas in images. However, these features suffer from high dimensional structure, in other words \"curse of dimensionality\" for further processes. Autoencoders (AE) are proposed in this study to solve the dimensionality reduction problem of visual features. To assess the efficacy, object recognition is performed using reduced dimensional visual features. For this purpose, dimensionalities of three well-known feature vectors, namely, HOG, SIFT and SURF, are reduced to half. Moreover, deep learning based features are also reduced. Then, reduced vectors, which are called as AE-HOG, AE-SIFT, AE-SURF and AE-DEEP are fed to object recognition task. Also, dimensionality reduction is implemented by a variant of AE, variational autoencoder (VAE) and PCA, which is the most studied unsupervised method for these features, and the results are compared. Furthermore, all experiments are repeated on noisy images. Results suggest that dimensionality reduction of these feature vectors can be accomplished successfully owing to the proposed method.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"37 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120994719","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}

{"title":"A Hadoop Based Framework for Soil Parameters Prediction","authors":"A. E. Mezouari, M. Najib","doi":"10.1109/SITIS.2019.00111","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00111","url":null,"abstract":"Nowadays, in view of the exponential growth of the populations, the consumption of the fresh water resources rises more and more. One can distinct different irrigation technique that have been implemented to optimize the consumption of freshwater resources in agriculture, such as flood irrigation and drip irrigation. As demand for freshwater rises, water availability decreases due to climate change. For this reason, most of the researchers are currently working on the automation of irrigation systems. These automated systems rely on the advances of machine learning, massive data and IoT techniques for building new innovative and effective solutions. Thus, the integration of predictive process represents a vital step for anticipating and assuming the adaptation to the impact of climatic changes in agriculture, through an accurate prediction of soil and environment features, and analysis of its dependencies as well. In this paper, we propose an adaptive online learning (OL) framework for supporting irrigation decision by soil features diagnosis and forecast with a focus on the implementation of Three prediction methods the extreme gradient boosting, random forest and the Auto Regressive Moving Average based on Hadoop/Map-Reduce environment to predict soil moisture, depending on soil temperature and time in various depth. At the end we discussed the accuracy of these methods in different conditions.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"412 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115917149","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}

{"title":"BigBank: A GIS Integrated AHP-TOPSIS Based Expansion Model for Banks","authors":"S. Sharmin, Khondokar Solaiman","doi":"10.1109/SITIS.2019.00058","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00058","url":null,"abstract":"Banks require consistent expansion through its lifetime to be competitive and reliable to their customers. But no previous branch expansion model considers both existing customers and branches when solves branch location problems. We propose BigBank model for branch location problem based on clustering-Analytic Hierarchy Process (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) where we consider both parties and their geographical positioning. After applying K-means clustering on uncovered customers, we take cluster centers as primary branch candidates and collect Geographic Information System (GIS) information about them. Then we use experts' opinions on branch location with four criteria and 12 different sub-criteria in the AHP method for ranking. Based on the ranking of the criteria of bank experts, our model computes the best possible location using the TOPSIS ranking method. We implement our model for a commercial bank in Bangladesh and show that our solution is always better in all three metrics considered in this literature from the traditional State of Art solution even in different fiscal years.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132615479","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}

{"title":"Workshop Messages","authors":"","doi":"10.1109/sitis.2019.00007","DOIUrl":"https://doi.org/10.1109/sitis.2019.00007","url":null,"abstract":"Computational Intelligence techniques are traditionally adopted in several different application domains such as industrial, medical, decision making, gaming to name but a few. Despite this growing diffusion, there are still many possible areas where computational intelligence application is partial or could be extended and improved, due to the actual limitations in terms of computational power or strict requirements in terms of assurance of the results. This workshop aims to investigate the impact of the adoption of advanced and innovative Computational Intelligence techniques in emerging application fields like IoT and Big Data. This edition of the workshop is focused primarily on industrial and health applications with special emphasis to real time systems grounded on Big Data ecosystems. The workshop will bring together researchers on different disciplines from academia and industry with a common objective: go beyond the frontiers of today applications of Computational Intelligence techniques. We are confident that it will constitute an excellent opportunity for the participant to engage in fruitful scientific and technical discussions. We have selected four full papers for presentation (36% rate of acceptance), which had a positive score during the reviewing process. All papers were assigned to three members of the program committee for review, and at least three reviews were recorded for each paper. We would like to thank the international program committee for the support in the reviewing process and for their helpful comments. The International Workshop on Distributed, Autonomic and Robust Wireless Networks (DARWiN) aims to gather researchers interested on wireless communications. It provides an opportunity to discuss ongoing research, new contributions and topical subjects. DARWiN covers major aspects in both theoretical (modeling, algorithmic) and experimental (simulation, emulation, real experimentation) fields. The main application domains are ad hoc networks, sensor networks, mesh networks and vehicular networks, but they can be extended to other types of wireless networks (especially low-resource networks). and very Their helpful and constructive remarks allowed the authors whose papers were accepted to improve the quality of their work. We hope that DARWiN will provide opportunities to attendees to exchange innovative ideas about their various practices and experiences, and hopefully also to initiate cooperative projects. and four papers will topics covered those are perception of sparkle in coatings, the use of VR headsets for vision research, Reflectance Transformation Iimaging and hyperspectral image interaction and visualization.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132639982","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}

{"title":"An Optimized Spectrum Sensing Implementation Based on SVM, KNN and TREE Algorithms","authors":"M. Saber, Abdessamad El Rharras, Rachid Saadane, Aroussi Hatim Kharraz, A. Chehri","doi":"10.1109/SITIS.2019.00068","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00068","url":null,"abstract":"Cognitive radio (CR) network is an intelligent technology, widely used to solve the scarcity of the radio spectrum by allowing the unlicensed users to have access to the licensed spectrum. Spectrum sensing (SS) phase is of great importance to the workings of a cognitive radio network (CRN). It consists in detecting licensed signals in a particular frequency band to decide whether the unlicensed signals can transmit or not. In order to detect primary user (PU) presence, this paper proposes a low cost and low power consumption spectrum sensing implementation based on real signals. These signals are generated by an ARDUINO UNO card and a 433 MHz Wireless transmitter (ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type). The reception interface is constructed using an RTL-SDR dongle connected to MATLAB software. The signal detection (spectrum sensing) is done by three methods: support vector machine (SVM), Decision Trees (TREE) and k-nearest neighbors (KNN). The main objective is to identify the best method for spectrum sensing between the three methods. The performance evaluation of our proposed model is the probability of detection (P_d) and the false alarm probability (P_fa). This Comparative work has shown that the SS operation by SVM and KNN can be more accurate than TREE and some other classical detectors.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122176194","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}

{"title":"Anticipation of Everyday Life Manipulation Actions in Virtual Reality","authors":"Fatemeh Ziaeetabar, Stephan Pfeiffer, M. Tamosiunaite, F. Wörgötter","doi":"10.1109/SITIS.2019.00074","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00074","url":null,"abstract":"While the comprehension of human actions by computer algorithms is widely used in various disciplines of science and technology, the need to predict the actions before their completion is growing. This prediction allows us to prevent undesirable events and enable an efficient interaction between humans and intelligent systems. Here, we first represent manipulation actions using the Enriched Semantic Event Chain (ESEC) framework which creates a temporal sequence of static and dynamic spatial relations between the objects and next, classify and predict the actions. In this paper, we are interested to compare the predictability power of the ESEC framework with that of human subjects. To this end, we designed an experiment in a virtual reality environment and created 300 video scenarios from 10 every day life manipulations. These While the comprehension of human actions by computer algorithms is widely used in various disciplines of science and technology, the need to predict the actions before their completion is growing. This prediction allows us to prevent undesirable events and enable an efficient interaction between humans and intelligent systems. Here, we first represent manipulation actions using the Enriched Semantic Event Chain (ESEC) framework which creates a temporal sequence of static and dynamic spatial relations between the objects and next, classify and predict the actions. In this paper, we are interested to compare the predictability power of the ESEC framework with that of human subjects. To this end, we designed an experiment in a virtual reality environment and created 300 video scenarios from 10 every day life manipulations. These data were next evaluated by both the framework and 50 human participants. The results were surprising because the framework predicted superior than the humans.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"397 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115204917","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}

{"title":"Using Entropy and Marr Wavelets to Automatic Feature Detection for Image Matching","authors":"Beibei Cui, Jean-Charles Créput","doi":"10.1109/SITIS.2019.00084","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00084","url":null,"abstract":"Image matching, also refereed as feature point matching, is a fundamental issue in computer vision. In this paper, we propose to use local entropy based on Marr wavelets within scale-interaction to improve the accuracy of automatic feature detection in the context of image matching. The goal is to improve the accuracy of the feature matching step while exhibiting a highly representative set of features of the objects within both images. To improve reliability, we propose to exploit local entropy under a mesh division strategy in combination with a sensitive feature selection stage. Experimental results show that this algorithm can outperform some of the conventional feature extraction algorithms with higher subsequent matching recall rate of image matching.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123442955","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}

{"title":"Grid Search Optimization (GSO) Based Future Sales Prediction for Big Mart","authors":"G. Behera, N. Nain","doi":"10.1109/SITIS.2019.00038","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00038","url":null,"abstract":"In retailer domain predicting sales before actual sales plays a vital role for any retailer company like Big Mart or Mall for maintaining a successful business. Traditional forecasting models such as statistical model is commonly used as methodology for future sales prediction, but these techniques takes much more time to estimate the sales, also they are not capable to handle the non-linear data. Therefore, Machine Learning(ML) techniques are employed to handle both non-linear and linear data. ML techniques can also efficiently large volume of data like Big Mart dataset, containing large number of customer data and individual data item's attribute. A retailer company wants a model that can predict accurate sales so that it can keep track of customers future demand and update in advance the sale inventory. In this work, we propose a Grid Search Optimization (GSO) technique to optimize the parameters and select the best tuning hyper parameters, further ensemble with Xgboost techniques for forecasting the future sales of a retailer company such as Big Mart and we found our model produces the better result.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129667410","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}

{"title":"Improving Probabilistic Flooding Using Topological Indexes","authors":"D. Kifle, G. Gianini, M. Libsie","doi":"10.1109/SITIS.2019.00067","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00067","url":null,"abstract":"Unstructured networks are characterized by constrained resources and require protocols that efficiently utilize bandwidth and battery power. Probabilistic flooding, allows nodes to rebroadcast RREQ packets with some probability p, thus reducing the overhead. The key issue in of this algorithm consists of determining p. The techniques proposed so far either use a fixed p determined by a priori considerations, or a p variable from one node to the other - set, for instance based on node degree or distance between source and destination - or even a dynamic p based on the number of redundant messages received by the nodes. In order to make the computation of forwarding probability p works optimally regardless of changing of topology, we propose to set p based on the node role within the message dissemination process. Specifically, we propose to identify such role based on the nodes' clustering coefficients (the lower the coefficient, the higher the forwarding probability). The performance of the algorithm is evaluated in terms of routing overhead, packet delivery ratio, and end-to-end delay. The algorithm pays a price in terms of computation time for discovering the clustering coefficient, however reduces unnecessary and redundant control messages and achieves a significant improvements in both dense and sparse networks in terms of packet delivery ratio. We compare by simulation the performance of this algorithm with the one of the most representative competing algorithms.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128977677","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}