Opportunistic Scheduling and Beamforming for MIMO-OFDMA Downlink Systems with Reduced Feedback

Opportunistic scheduling and beamforming schemes with reduced feedback are proposed for MIMO-OFDMA downlink systems. Unlike the conventional beamforming schemes in which beamforming is implemented solely by the base station (BS) in a per-subcarrier fashion, the proposed schemes take advantages of a novel channel decomposition technique to perform beamforming jointly by the BS and the mobile terminal (MT). The resulting beamforming schemes allow the BS to employ only {\em one} beamforming matrix (BFM) to form beams for {\em all} subcarriers while each MT completes the beamforming task for each subcarrier locally. Consequently, for a MIMO-OFDMA system with $Q$ subcarriers, the proposed opportunistic scheduling and beamforming schemes require only one BFM index and $Q$ supportable throughputs to be returned from each MT to the BS, in contrast to $Q$ BFM indices and $Q$ supportable throughputs required by the conventional schemes. The advantage of the proposed schemes becomes more evident when a further feedback reduction is achieved by grouping adjacent subcarriers into exclusive clusters and returning only cluster information from each MT. Theoretical analysis and computer simulation confirm the effectiveness of the proposed reduced-feedback schemes.Comment: Proceedings of the 2008 IEEE International Conference on Communications, Beijing, May 19-23, 200

Axion dark matter search using the storage ring EDM method

We propose using the storage ring EDM method to search for the axion dark matter induced EDM oscillation in nucleons. The method uses a combination of B and E-fields to produce a resonance between the $g-2$ spin precession frequency and the background axion field oscillation to greatly enhance sensitivity to it. An axion frequency range from $10^{-9}$ Hz to 100 MHz can in principle be scanned with high sensitivity, corresponding to an $f_a$ range of $10^{13}$ GeV $\leq f_a \leq 10^{30}$ GeV, the breakdown scale of the global symmetry generating the axion or axion like particles (ALPs)

Evolution of RF-signal cognition for wheeled mobile robots using pareto multi-objective optimization

This article describes a simulation model in which a multi-objective approach is utilized for evolving an artificial neural networks (ANNs) controller for an autonomous mobile robot. A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that could optimize two objectives in a single run; (1) maximize the mobile robot homing behavior whilst (2) minimize the hidden neurons involved in the feed-forward ANN. The generated controllers are evaluated on its performances based on Pareto analysis. Furthermore, the generated controllers are tested with four different environments particularly for robustness assessment. The testing environments are different from the environment in which evolution was conducted. Interestingly however, the testing results showed some of the mobile robots are still robust to the testing environments. The controllers allowed the robots to home in towards the signal source with different movements’ behaviors. This study has thus revealed that the PDE-EMO algorithm can be practically used to automatically generate robust controllers for RFlocalization behavior in autonomous mobile robots

A Review of Automatic License Plate Recognition System in Mobile based Platform

Automatic license plate recognition (ALPR) is the process of retrieving license plate information from a captured image or video frames from a sequence of videos. ALPR can assist law enforcement officers to identify stolen vehicles or to capture vehicle information from those that violate traffic laws instantly. It is also commonly used as an electronic payment system for toll payment or parking fee payment. Traditionally, ALPR is installed in a PC-based platform to take advantage of its processing power to process high-quality images captured by high-resolution cameras. Most smartphones nowadays are equipped with a high-quality camera and faster processing system which can be used to develop portable ALPR system. Thus, this has encouraged many researchers to work on implementing ALPR technology for the mobile platform. In this paper, we reviewed several researches that have implemented ALPR in the mobile-based platform. We discuss the techniques used in the three main stages of ALPR namely localisation, segmentation and recognition. The advantages and disadvantages of each technique are summarised in a table

Likelihood of Stachybotyrs atra sensitization in Canadian populations

Stachybotrys atra has achieved great notoriety recently as a mould capable of producing mycotoxin, a potentially quite harmful substance. Because of news reports, patients have become quite concerned about “mould allergy” as the cause of an increasing number of symptoms. We set out to discover what percentage of patients referred to regional Allergy clinics have become sensitized to moulds, but especially Stachybotrys atra

An evolutionary based features construction methods for data summarization approach

Coral reefs are on course to become the first ecosystem that human activity will eliminate entirely from the Earth, a leading United Nations scientist claims. It is predicted that this event will occur before the end of the present century, which means that there are children already born who will live to see a world without coral. Coral reefs are important for the immense biodiversity of their ecosystems. They contain a quarter of all marine species. This research addresses the question whether a data summarization approach can be utilized to predict the survival of Coral Reefs in Malaysia by identifying the survival factors for these Coral Reefs. A data summarization approach is proposed due to its capability to learn data stored in multiple tables. In other words, this research will discuss the application of genetic algorithm to optimize the feature construction process from the Coral Reefs data to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). The DARA algorithm will be applied to summarize data stored in the non-target tables by clustering them into groups, where multiple records stored in non­target tables correspond to a single record i,tored in a target table. Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm.This research proposes novel feature construction methods, called Variable Length Feature Construction without Substitution (VLFCWOS) and Variable Length Feature Construction with Substitution(VLFCWS), in order to construct a set of relevant features in learning relational data. These methods are proposed to improve the descriptive accuracy of the summarized data. In the process of summarizing relational data, a genetic algorithm is also applied and several feature scoring measures are evaluated in order to find the best set of relevant constructed features. In this work, we empirically compare the predictive accuracies of classification tasks based on the proposed feature construction methods and also the existing feature construction methods. The experimental results show that the predictive accuracy of classifying data that are summarized based on VLFCWS method using Total Cluster Entropy combined with Information Gain (CE-JG) as feature scoring outperforms in most cases

The customer choice model of commercial retailers based on MarKov analysis

The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity