Sparse Reconstruction-based Detection of Spatial Dimension Holes in Cognitive Radio Networks

In this paper, we investigate a spectrum sensing algorithm for detecting spatial dimension holes in Multiple Inputs Multiple Outputs (MIMO) transmissions for OFDM systems using Compressive Sensing (CS) tools. This extends the energy detector to allow for detecting transmission opportunities even if the band is already energy filled. We show that the task described above is not performed efficiently by regular MIMO decoders (such as MMSE decoder) due to possible sparsity in the transmit signal. Since CS reconstruction tools take into account the sparsity order of the signal, they are more efficient in detecting the activity of the users. Building on successful activity detection by the CS detector, we show that the use of a CS-aided MMSE decoders yields better performance rather than using either CS-based or MMSE decoders separately. Simulations are conducted to verify the gains from using CS detector for Primary user activity detection and the performance gain in using CS-aided MMSE decoders for decoding the PU information for future relaying.Comment: accepted for PIMRC 201

Vendor rating for an entrepreneur development programme: a case study using the analytic hierarchy process method

With collaborative purchasing programmes where one of the aims is to develop suppliers, vendor rating is important not only in supplier selection and in deciding how to allocate business but also to determine where scarce development effort is best applied. This paper describes a case study into vendor rating for a government sponsored Entrepreneur Development programme in Malaysia. The paper reviews current methods for vendor rating and finds them wanting. It illustrates a new approach based on the use of Saaty's Analytic Hierarchy process method, which was developed to assist in multi-criteria decision problems. The new method overcomes the difficulties associated with the categorical and simple linear weighted average criteria ranking methods. It provides a more systematic way of deriving the weights to be used and for scoring the performance of vendors

Risk management strategies using seasonal climate forecasting in irrigated cotton production: a tale of stochastic dominance

Decision‐making in agriculture is carried out in an uncertain environment with farmers often seeking information to reduce risk. As a result of the extreme variability of rainfall and stream‐flows in north‐eastern Australia, water supplies for irrigated agriculture are a limiting factor and a source of risk. The present study examined the use of seasonal climate forecasting (SCF) when calculating planting areas for irrigated cotton in the northern Murray Darling Basin. Results show that minimising risk by adjusting plant areas in response to SCF can lead to significant gains in gross margin returns. However, how farmers respond to SCF is dependent on several other factors including irrigators’ attitude towards risk.Crop Production/Industries, Risk and Uncertainty,

Kinetic Study of the Hydrolysis of synthesized Ibuprofen Ester and its Biological Activity

It is known that the oral administration of ibuprofen caused an irritation of stomach as a side effect due to its carboxylic moiety. Ibuprofen ester was synthesized by linking the carboxylic moiety of ibuprofen and the hydroxylic group of paracetamol to reduce its side effect. Study the kinetic hydrolysis of prepared ester was examined at different values of physiological pH (1.0, 5.8, 6.4 and 7.4) at 37 ± 0.1 of 1 hour period. Measurements of absorbance were carried out by UV-Visible spectrophotometer to follow the stability of ester, it showed Pseudo first order hydrolysis. The pH- apparent rate profiles of ester was exhibited a good stability at pH 1.0 and pH 5.8. Pharmacological activity in vivo of prepared ester was evaluated in relation to analgesic and anti-inflammatory activity using the acetic acid method and the hind paw oedema inhibition, respectively. Acetyl salicylic acid (aspirin) was used as a reference drug for the above tests. The synthesized ester showed higher analgesic and anti-inflammatory action than aspirin

Multiclass Sequential Feature Selection and Classification Method for Genomic Data

This paper presents an efficient multiclass sequential feature selection and classification (mk-SS) method using gene expression signatures. The development of this method employs 10-fold cross-validation to ensure stability. The efficiency of this method is assessed through the misclassification error rate and some other performance measures. The performances of the mk-SS were compared with the classification results of the Support Vector Machines (SVM) over five published multiclass microarray datasets. The results showed that the mk-SS method efficiently selects the informative gene biomarkers for proper classification of the biological groups of the tissue samples. This method competes favourably with SVM in terms of prediction accuracy while it outperforms the SVM in 80% of cases considered. The quality of the features selected by mk-SS algorithm was validated by hybridizing the feature selection scheme of the mk-SS into the standard SVM algorithm which significantly improves the predictive power of the standard SVM method. This work has shown that classification of various cancer type using gene expression profiles is feasible especially when the endpoints are of multi-category. Keywords: k-SS, mk-SS, Support Vector Machines, Microarray, Misclassification error rat