Emergence of Wireless Sensor Networks provided the ability to connect, collect and disseminate information across various sensor nodes. VANETs turned out to act as a boon to enhance the safety and non-safety aspects of transportation domain, giving way to the future of Intelligent Transport Systems. To generate cooperative awareness in the network, VANETs use beacons, which are small packets of information transmitted as BSMs (Basic Safety Messages). Beaconing was developed in the initial phases of development of VANETs and mainly suffers a trade-off between channel congestion and the level of accuracy of exchanged information. In this work, we propose an adaptive speed based beaconing approach, which uses probability as a means to answer two key questions. First is whether to beacon or not and second is at what rate beaconing should be done to reduce channel congestion and increase the accuracy of information. We compare the results with an adaptive density-based approach and with normal static beaconing cases. Performance evaluation on Veins framework demonstrates that it gives better results as compared to both the other approaches. We compare the results concerning generated BSMs, received BSMs and total packet loss. The simulation is modeled to make it as realistic as possible by introducing a vast heterogeneous network with random vehicle mobility trips.
Authors: Sarishma . (Assistant Professor), Ravi Tomar (University of Petroleum and Energy Studies, India), Sandeep Kumar (Sunderdeep Engineering College, Ghaziabad, India), Mukesh Awasthi (School of Physical and Decision Sciences, BBAU, Lucknow, India),
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Traffic prediction has been extensively studied in the past decades. Vehicle’s speed is considered the main factor for traffic fore- casting, but external parameters, such as the weather, can also have a strong impact. This is a case of a classification problem to which Ma- chine Learning has shown to have strong solving potential, if trained properly. In this paper, we propose a two-level model related to traffic forecasting parameters : It is necessary that there is no missing data in the training set, then train a Neural Network able to accurately predict the traffic situation Three completion algorithms from different types (Machine learning, algebraic and statistical methods) are compared for the rebuilding of the training set. The set is then used to train a Convolutional Neural Network into predicting the state of the traffic the way a human would do. The model is evaluated on the two parts: How accurately it can complete the data set and how correct the predictions are. This work is part of the ongoing research on intelligent vehicles that are capable of determining the context of the driving environment.
Authors: Abderraouf Khezaz (ECE Paris, Université de Versailles St-Quentin-en-Yvelines), Manolo Dulva Hina (ECE Paris School of Engineering), Hongyu Guan (Université de Versailles St-Quentin-en-Yvelines), Amar Ramdane-Cherif (Université de Versailles St-Quentin-en-Yvelines),
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The dynamics of a prosthesis is an important parameter to consider in order to al-low efficient use. In fact, the prosthesis should perform the desired task with enough precision under a controlled kinematics. To this aim, it is necessary to set up a control law which guarantees the operating of the structure according to a pre-established specification. A prosthesis like any mechatronic structure is a complex and dynamic system, based on parameters which can evolve as a func-tion of interactions, physical conditions or time, for this it is necessary to use a robust control algorithm to address this issue. In this work we present a sliding mode control algorithm applied to an anthropomorphic prosthesis printed in 3D at the LRPE / LISV laboratories. The control structure used enables to overcome the modelling uncertainties and parametric variations of the mechatronic system such as the coefficients of friction in the joints and the coefficient of elasticity of cable-pull.
Authors: sofiane BENCHABANE, Nadia SAADIA (University of Science and Technology Houari Boumediene, Algeria), Amar RAMDANE-CHERIF (Versailles Saint-Quentin-en-Yvelines University, France),
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In Natural Language Processing, labeling a text corpus is often an expensive task that requires a lot of human efforts and cost. Whereas unlabeled text corpora in varying domains are readily available. For a couple of decades, research efforts have concentrated on algorithms that can be used for labeling the corpus, thus minimizing the number of articles required to be labeled manually. Semi-Supervised Learning and Active Learning have been a great promise for labeling the articles using a trained model. Also, Semi-Supervised learning algorithms and Active learning algorithms have strong theoretical guarantees. This study aims to tag 1183 articles from The New York Times and The Wall Street Journal with the subject (i.e. primary organization related to news articles) employing Active Learning algorithm. We used Active Learning algorithm which uses Random Sampling along with Uncertainty Based Querying. This Active Learning approach is used to train Naive Bayes classifier using Bag of Words features. This classifier is used to tag 1183 articles of which only 167 required manual review, thus achieving reduction of 85.89% with 78.18% accuracy. Also, for verifying quality of labeled corpus, SVM classifier using same features was trained on labeled corpus giving accuracy of 74.45% on test data.
Authors: Yash Patel (New Jersey Institute of Technology), Meet Parekh (CBInsights),
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With the growth of Wi-Fi by the time, performance and quality are facing quite a lot of challenges. Wi-Fi works on the principle of the IEEE 802.11 based carrier sense multiple access –collision avoidance (CSMA/CA) to transmit packets and distributed coordination function (DCF) protocol based on Inter-frame spacing used to make the gap between two frames. In this manner, an 802.11 device gets limited time in the environment to finish its activity, with a rapid increase in the number of devices in the 802.11 environments. The important operation is always a connection of Wi-Fi device with another device, another device can be an Ac-cess point or any other Wi-Fi device (ad-hoc or Wi-Fi direct mode). Several re-searchers work on why connection time is more important and what factors affect its most [1,2]. In this paper, we describe a way to reduce connection time with the use of cross-layer approach via offloading DHCP work to the MAC layer.
Authors: Raghava Nallanthighal (Delhi Technological University), Vishal Bhargava (Delhi Technological University),
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The distribution network power system is being encountered by rapidly growing load demand and it is observed that under certain critical loading conditions, the distribution system poses maximum power losses and poor voltage profile and collapse in certain areas. To overcome these problems incorporating distributed generation (DGs) on the grid near to the load center is the better solution as com-pared to others. However, for the DG to serve its purpose, its location and size have to be determined optimally. In this paper, Grid-Based Multi-Objective Har-mony Search Algorithm (GrMHSA) has been utilized to determine the size and location of DG in the distribution system in Debre Markos town. By placing DG optimally, in addition to the reduction of the power loss in the distribution net-work, the proposed mechanism improves the node (bus) voltage profile of the system under consideration. A MATLAB program is developed to mitigate pow-er losses and improve the voltage profile by optimally sizing and placing a DG in the distribution network. After sizing and placing the DG in the network, the total voltage deviation, active and reactive power losses are reduced by 93.42%, 81.63% and 82.45% for Debre Markos Feeder 3 and 85.20%, 84.94% and 85.73% for Debre Markos Feeder 4 respectively. The performance comparison of GrMHSA and MOPSO has been made and GrMHSA has been found better in terms of reducing voltage deviation and power losses in the system.
Authors: Takele Agajie (Department of Electrical and Computer Engineering, Debre Markos University, Debre Markos, Ethiopia), Yayehyirad Awoke (Department of Electrical and Computer Engineering, Debre Markos University, Debre Markos, Ethiopia), Tesfaye Anteneh (Department of Electrical and Computer Engineering, Debre Markos University, Debre Markos, Ethiopia), Engidaw Hailu (Department of Electrical and Computer Engineering, Debre Markos University, Debre Markos, Ethiopia), Ghantasala Srinivas Rao (Department of Electrical and Computer Engineering, Debre Markos University, Debre Markos, Ethiopia), Fsaha Gebru (Department of Electrical and Computer Engineering, Haramaya University, Haramaya, Ethiopia),
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