WiMAX Basics From Deployments to PHY Improvements

© ASEE 2014WiMAX (Worldwide Interoperability for Microwave Access) is an emerging broadband wireless technology for providing Last mile solutions for supporting higher bandwidth and multiple service classes with various quality of service requirement. The unique architecture of the WiMAX MAC and PHY layers that uses OFDMA to allocate multiple channels with different modulation schema and multiple time slots for each channel allows better adaptation of heterogeneous user’s requirements. The main architecture in WiMAX uses PMP (Point to Multipoint), Mesh mode or the new MMR (Mobile Multi hop Mode) deployments where scheduling and multicasting have different approaches. In PMP SS (Subscriber Station) connects directly to BS (Base Station) in a single hop route so channel conditions adaptations and supporting QoS for classes of services is the key points in scheduling, admission control or multicasting, while in Mesh networks SS connects to other SS Stations or to the BS in a multi hop routes, the MMR mode extends the PMP mode in which the SS connects to either a relay station (RS) or to Bs. Both MMR and Mesh uses centralized or distributed scheduling with multicasting schemas based on scheduling trees for routing. In this paper a broad study is conducted About WiMAX technology PMP and Mesh deployments from main physical layers features with differentiation of MAC layer features to scheduling and multicasting approaches in both modes of operations

Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks

The evolution of the social media and the e-commerce sites produces a massive amount of unstructured text data on the internet. Thus, there is a high demand to develop an intelligent model to process it and extract a useful information from it. Text classification plays an important task for many Natural Language Processing (NLP) applications such as, sentiment analysis, web search, spam filtering, and information retrieval, in which we need to assign single or multiple predefined categories to a sequence of text. In Neural Network Language Models learning long-term dependencies with gradient descent is difficult due to the vanishing gradient problem. Recently researchers started to increase the depth of the network in order to overcome the limitations of the existing techniques. However, increasing the depth of the network means increasing the number of the parameters, which makes the network computationally expensive, and more prone to overfitting. Furthermore, NLP systems traditionally treat words as discrete atomic symbols; the model can leverage small amounts of information regarding the relationship between the individual symbols. In recent years, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to language modeling with comparative, remarkable results. CNNs are a noble approach to extract higher-level features invariant to local translation. However, this method requires the stacking of multiple convolutional layers in order to capture long-term dependencies because of the locality of the convolutional and pooling layers. In this dissertation, we introduce a joint CNN-RNN framework to overcome the problems in the existing deep learning models. Briefly, we applied an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via Long-Short-Term Memory (LSTM). Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review (IMDB) dataset, and the Stanford Sentiment Treebank (SSTb) dataset. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer with no pooling layers. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an efficient framework that has fewer parameters and a high level of performance

Journal of nematode morphology and systematics

The aim of quantum cryptography is to overcome the everlasting problem of unrestricted security in private communication. The usage of the quantum principles protects the privacy of the user data during the time it is in the transmission process over the telecommunication channels. The sophisticated algorithm we have developed will make the data meaningless to eavesdroppers. The security of modern cryptography systems has been accomplished by using a long key that will require many years to launch a brute force attack. Therefore, we designed an efficient algorithm that is developed based on BB84 and B92 techniques. In this paper, we utilized the classic features of quantum mechanism, such as superposition and uncertainty principle. We present the underlining mechanisms of quantum cryptography that enhances the security of data transmission in three stages with valid results that promise a low rate of errors that leads to a strong consistent key by raising the constraint of the security concept

Incentive Mechanisms for Crowdsensing

Crowd sensing is a mechanism that facilitates the company to accomplish by depurating the people. It provides the temporary and voluntary service supporter. However, crowdsensing experiences the problem due to user selection and payment determination. Thus problems deteriorate the incompleteness of task at present. This paper introduces more than one algorithms: The User Selection Mechanism (USM) and the Payment Determination Mechanism (PDM) that ensure the task complexity, automatic task arrangement and final judgment for the task completeness. Through rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed allocation strategies achieve. Furthermore, our proposal algorithm is compared with traditional approaches. Based the results, improvements in task complexity, automatic task management and task completeness is observed

Secure and Energy Efficient Mobile Payment Protocol

Electronic commerce has become integral part of business operation and individual person\u27s life. It is easy, fast and reliable way of money transfer. However, with new technology security related issues have increased drastically. In this research, we have proposed secured payment protocol using self-certified key generation method. To measure performance of used payment procedure in payment application, one should understand flaws in depth in payment application. This research analyzes the security related issues of application and proposed new secure and energy efficient payment protocol (SEEMPP). We applied the reverse engineering method to inspect the risks related to social engineering attacks. Furthermore, we proposed an energy-efficient model to improve energy efficiency for payment application. This results in a better payment application which will gain customers trust and will increase e-commerce business