Universiti Teknologi Petronas

About: Universiti Teknologi Petronas is a education organization based out in Ipoh, Malaysia. It is known for research contribution in the topics: Adsorption & Ionic liquid. The organization has 6127 authors who have published 11284 publications receiving 119400 citations.

Emerging Smart Logistics and Transportation Using IoT and Blockchain

Abstract: Transportation and logistics management play a vital role in the development of a country With the advancement of the Internet of Things (IoT) devices, smart transportation is becoming a reality However, these abundant connected IoT devices are vulnerable to security attacks Recently, Blockchain has emerged as one of the most widely accepted technologies for trusted, secure and decentralized intelligent transportation systems This research study aims to contribute to the field of logistics and transportation by exploring the potential of IoT and Blockchain technology in smart logistics and transportation We propose a layered framework, namely BCTLF, for smart logistics and transportation that integrates IoT and Blockchain to provide an intelligent logistics and transportation system Finally, we present two real-life IoT and Blockchain-based case studies to highlight the contribution of IoT and Blockchain in logistics and transportation

Classification of EEG Signals Based on Pattern Recognition Approach.

Abstract: Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a ‘pattern recognition’ approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher’s discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven’s Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP) and Naive Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39 % for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90 to 7.81 Hz). Accuracy rates for MLP and Naive Bayes classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.