Fully generalised spatial modulation Elsayed, Mohamed; Hussein, Hany S.; Mohamed, Usama Sayed
2018 35th National Radio Science Conference (NRSC),
2018-March
Conference Proceeding
This paper introduces a novel multiple-input-multiple-output (MIMO) modulation technique named fully-generalised spatial modulation (F-GSM). The proposed F-GSM vanquishes the pivotal criticism of the ...conventional spatial modulation (SM) and generalised spatial modulation (GSM) techniques which constraints the data rate increment to be proportionated with the base-two logarithm of the number of the transmit antennas (N t ). This logarithmical proportion regrettably, makes the improvement in the data rate of SM/GSM is not feasible compared to the increment in the number of transmit antennas. However, the proposed F-GSM achievable data rate is linearly proportional with N t . Thus, the proposed F-GSM achieves a higher data rate with lower number of N t compared to the conventional SM and GSM as well. The average bit error rate (ABER) performance and the computational complexity of the proposed F-GSM are tested reference to the conventional GSM technique at different spectral efficiency and with different number of N t . Simulation results corroborate the surpasses of the proposed F-GSM as it provides a superior enhancement in both BER performance and achievable data rate along with a reasonable computational complexity compared to the conventional GSM.
Challenges of managing and controlling wireless sensor networks (WSNs) includes efficient routing, increasing sensor node lifetime by conserving energy consumption, and base stations fault-tolerance. ...These challenges can be solved by help of Minimum Dominating Set (MDS) algorithms. By applying MDS to WSNs, only base stations (dominating sensors) take the burden of the communication instead of all sensors, and consequently saving the communication bandwidth, energy consumption, and increase the network lifetime. In addition to that, having multiple MDS improve base stations fault tolerance by scheduling different MDS periodically. In this research, a new Differential Evolutionary (DE) is composed to solve the general MDS problem. First, this problem is formulated as a binary optimization problem. Then, new evolutionary operations are introduced to fit the considered problem and wireless sensor network applications. Beside these evolutionary operations, other search enhancement operations are invoked to improve the search diversification and intensification process in addition to enabling the search process to find several distinct solutions. Those solutions can help in scheduling the network control nodes in order to increase its lifetime. Several experimental simulations over benchmark networks are carried out to test the efficiency of the proposed method. The results demonstrate the quality of the proposed method for obtaining minimum dominating sets for the considered test networks.
In this work, a new approach for time-to-digital converter (TDC) was implemented and measured. The TDC is utilizing two ring oscillators, slow and fast oscillators. Ring oscillators that are used in ...the proposed scheme are implemented utilizing fast lookup tables. The editor floor planning was used to optimize the logic components placement and routing. The suggested TDC design was implemented and tested utilizing a Xilinx Virtex-5 field-programmable gate array. To ensure the effectiveness of the suggested design, the proposed system has been tested by both the simulation environment and the hardware measurement bench. The convergence between simulation results and measurement results reflects the accuracy and reliability of the proposed scheme. The TDC achieves a measured accuracy of 4 ps. The obtained results show the superiority of the proposed system compared to the related work.
Given the rapid advancement of computer technology, the importance of administering adaptive tests with polytomous items is in great need. With regard to the applicability of adaptive testing using ...polytomous IRT models, adaptive testing can use polytomous items of either rating scales, or in some testing situations of multiple choice. Additionally, the availability of computerized polytomous scoring of open-ended items enhances such applicability. This need promotes the research in polytomous adaptive testing (PAT). This dissertation is an effort to focus on item selection methods, as a major component, in polytomous computerized adaptive testing. So, it consists of five chapters that cover the following: Chapter 1 focuses on a thorough introduction to the item response theory (IRT) models and adaptive testing related to polytomous items. Such an important overview and introduction to basic concepts in test theory and mathematical models for polytomous items is needed for the flow of consequent chapters. Chapter 2 is devoted to the development of a central location index (LI) to uniquely represent the polytomous item with a scale value parameter using most commonly used polytomous models. The motivation and rationale to search for a central or an overall location parameter is twofold: (a) the confusion of multiple and different parameterizations for a polytomous item even for the same model, and (b) the unavailability of such single location parameter block the usage of certain item selection methods in adaptive testing. Two approaches are used to derive the proposed LIs, one is based on the item category response functions (ICRFs) and the other is based on the polytomous item response function (IRF). As a result, four LIs are proposed. Chapter 3 is particularly assigned to development of an item selection method based on the developed location index and primarily assess its performance in the PAT context relative to existing methods. This method belongs to the non-information based item selection methods and we referred it as Matching-LI method. The results support that this proposed method is promising and is capable to produce accurate ability estimates and successfully manage the item pool usage. Chapter 4 introduces new item selection methods taking in consideration the previous chapter's results. The new methods are the hybrid, stage-based information, polytomous a-stratification methods. The first two methods try to merge more than one criterion for selecting items of each PAT (e.g., the hybrid method merges both the Matching-LI and maximum information (MI) methods). The last method uses Matching-LI method within each stratum. Chapter 5 provides discussion, conclusions, and limitations and future research directions with respect to important components of an adaptive testing program (i.e., item selection methods, item response models, item banks, and trait versus attribute estimation).
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