SRA-MSDU: Enhanced A-MSDU frame aggregation with selective retransmission in 802.11n wireless networks

The main goal of the IEEE 802.11n standard is to achieve more than 100 Mbps of throughput at the MAC service access point. This high throughput has been achieved via many enhancements in both the physical and MAC layers. One of the MAC enhancements is the frame aggregation in which multiple frames are concatenated into a single large frame before being transmitted. The 802.11n MAC layer defines two types of aggregation, aggregate MAC service data unit (A-MSDU) and aggregate MAC protocol data unit (A-MPDU). The A-MPDU outperforms A-MSDU due to its large aggregation size and the subframes retransmission in erroneous channels. However, in error free channels and under the same aggregation size the A-MSDU performs better than the A-MPDU due to its smaller headers. Thus, adding a selective retransmission capability to the A-MSDU would improve the system performance. In this paper, we have proposed an MSDU frame aggregation scheme that enables selective retransmission at the MSDU level without altering the original MAC header. In this proposed scheme an implicit sequence control mechanism has been introduced in order to keep the frames in sequence and preserve their correct order at the receiver side. The results show that the proposed scheme improves the system performance in terms of throughput and delay even under highly erroneous channels.     

Behavioral Attestation for Web Services using access policies

Service Oriented Architecture with underlying technologies like web services and web service orchestration opens new vistas for integration among business processes operating in heterogeneous environments. However, such dynamic collaborations require a highly secure environment at each respective business partner site. Existing web services standards address the issue of security only on the service provider platform. The partner platforms to which sensitive information is released have till now been neglected. Remote Attestation is a relatively new field of research which enables an authorized party to verify that a trusted environment actually exists on a partner platform. To incorporate this novel concept in to the web services realm, a new mechanism called WS-Attestation has been proposed. This mechanism provides a structural paradigm upon which more fine-grained solutions can be built. In this paper, we present a novel framework, Behavioral Attestation for Web Services, in which XACML is built on top of WS-Attestation in order to enable more flexible remote attestation at the web services level. We propose a new type of XACML policy called XACML behavior policy, which defines the expected behavior of a partner platform. Existing web service standards are used to incorporate remote attestation at the web services level and a prototype is presented, which implements XACML behavior policy using low-level attestation techniques.     

Ant-based service selection framework for a smart home monitoring environment

Selecting ambient media services in a smart home monitoring environment is challenging. Services in such an environment should be ubiquitous, adaptive, and robust with respect to access and delivery. Many different techniques exist for selecting services in smart environments, for example, dynamic programming, genetic algorithms, and fuzzy logic. However, existing approaches to service selection fail to address the dynamic nature of the services and the requirement of considering the user context and user satisfaction. We address this issue by proposing an ant-inspired service selection framework based on dynamic user preferences and satisfaction. This ant-inspired approach is robust to failures and adaptive to dynamic context. The proposed framework enables different categories of residents (e.g., elderly people, fathers with children, mothers, and so on) to access various media services in such a way that their experiences are optimized with regard to their surrounding environment. Experimental results demonstrate the viability of the proposed framework.     

Genetic algorithm‐based neural‐network modeling approach applied to AlGaN/GaN devices

An accurate equivalent circuit large‐signal model (ECLSM) for AlGaN‐GaN high electron mobility transistor (HEMT) is presented. The model is derived from a distributed small‐signal model that efficiently describes the physics of the device. A genetic neural‐network‐based model for the gate and drain currents and charges is presented along with its parameters extraction procedure. This model is embedded in the ECLSM, which is then implemented in CAD software and validated by pulsed and continuous large‐signal measurements of on‐wafer 8 × 125‐μm GaN on SiC substrate HEMT. Pulsed IV simulations show that the model can efficiently describe the bias dependency of trapping and self‐heating effects. Single‐ and two‐tone simulation results show that the model can accurately predict the output power and its harmonics and the associated intermodulation distortion (IMD) under different input‐power and bias conditions. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

A model-based survey of alert correlation techniques

As telecommunication networks evolve rapidly in terms of scalability, complexity, and heterogeneity, the efficiency of fault localization procedures and the accuracy in the detection of anomalous behaviors are becoming important factors that largely influence the decision making process in large management companies. For this reason, telecommunication companies are doing a big effort investing in new technologies and projects aimed at finding efficient management solutions. One of the challenging issues for network and system management operators is that of dealing with the huge amount of alerts generated by the managed systems and networks. In order to discover anomalous behaviors and speed up fault localization processes, alert correlation is one of the most popular resources. Although many different alert correlation techniques have been investigated, it is still an active research field. In this paper, a survey of the state of the art in alert correlation techniques is presented. Unlike other authors, we consider that the correlation process is a common problem for different fields in the industry. Thus, we focus on showing the broad influence of this problem. Additionally, we suggest an alert correlation architecture capable of modeling current and prospective proposals. Finally, we also review some of the most important commercial products currently available.     

Power Control for Cognitive Radio Networks: A Game Theoretic Approach

In communication industry one of the most rapidly growing area is wireless technology and its applications. The efficient access to radio spectrum is a requirement to make this communication feasible for the users that are running multimedia applications and establishing real-time connections on an already overcrowded spectrum. In recent times cognitive radios (CR) are becoming the prime candidates for improved utilization of available spectrum. The unlicensed secondary users share the spectrum with primary licensed user in such manners that the interference at the primary user does not increase from a predefined threshold. In this paper, we propose an algorithm to address the power control problem for CR networks. The proposed solution models the wireless system with a non-cooperative game, in which each player maximize its utility in a competitive environment. The simulation results shows that the proposed algorithm improves the performance of the network in terms of high SINR and low power consumption.

     

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Journal of Signal Processing Systems - Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful...     

Query-oriented topical influential users detection for top-k trending topics in twitter

Applied Intelligence - Online Social Networks (OSNs) have become inevitable for any new methodology both for viral promoting applications and instructing the creation of inciting information and...     

Different Estimation Methods for Constant Stress Accelerated Life Test under the Family of the Exponentiated Distributions

The Accelerated Life Testing (ALT) has been used for a long time in several fields to obtain information on the reliability of product components and materials under operating conditions in a much shorter time. One of the main purposes of applying ALT is to estimate the failure time functions and reliability performance under normal conditions. This paper concentrates on the estimation procedures under ALT and how to select the best estimation method that gives accurate estimates for the reliability function. For this purpose, different estimation methods are used, such as maximum likelihood, least squares (LS), weighted LS, and probability weighted moment. Moreover, the reliability function under usual conditions is predicted. The estimation procedures are applied under the family of the exponentiated distributions in general, and for the exponentiated inverted Weibull (EIW) as a special case. Numerical analysis including simulated data and a real life data set is conducted to compare the performances between these four methods. It is found that the ML method gives the best results among other estimation methods. Finally, a comparison between the EIW and the Inverted Weibull (IW) distributions based on a real life data set is made using a likelihood ratio test. It is observed that the EIW distribution can provide better fitting than the IW in case of ALT. Copyright © 2015 John Wiley & Sons, Ltd.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.