Electrical and Computer Engineering Commons^™

Computer Aided Pronunciation Learning For Al-Jabari Method: A Review, Noor Jamaliah Ibrahim, Mohd Yamani Idna Idris, Zulkifli Mohd Yusoff

Noor Jamaliah Ibrahim

Speech processing for Quranic Arabic is an active field of research, since a few years ago. We propose in this paper, a review on the potential of automatic speech recognition (ASR) computer-based technology to support Quranic learning processes. Thus, the aspects of Computer-Aided Pronunciation Learning (CAPL) are discussed, focusing towards the implementation on Quranic learning through Al-Jabari method. We believe Al-Jabari method is fast and efficient, as well as a practical way of learning Al-Quran. The analysis of CAPL systems towards the implementation in Quranic learning through Al-Jabari method are discussed in details.

Estimating Effective Connectivity From Fmri Data Using Factor-Based Subspace Autoregressive Models, Chee-Ming Ting Phd, Abd-Krim Seghouane Phd, Sh-Hussain Salleh Phd, Alias M. Noor Phd

Chee-Ming Ting

Modeling And Estimation Of Single-Trial Event-Related Potentials Using Partially Observed Diffusion Processes, Chee-Ming Ting Phd, Sh-Hussain Salleh, Z. M. Zainuddin, Arifah Bahar

Chee-Ming Ting

This paper proposes a new modeling framework for estimating single-trial event-related potentials (ERPs). Existing studies based on state-space approach use discrete-time random-walk models. We propose to use continuous-time partially observed diffusion process which is more natural and appropriate to describe the continuous dynamics underlying ERPs, discretely observed in noise as single-trials. Moreover, the flexibility of the continuous-time model being specified and analyzed independently of observation intervals, enables a more efficient handling of irregularly or variably sampled ERPs than its discrete-time counterpart which is fixed to a particular interval. We consider the Ornstein–Uhlenbeck (OU) process for the inter-trial parameter dynamics and …

Introduction To Anti-Collision Algorithms And Estimation Methods In Rfid Systems, Masoud Shakiba

Masoud Shakiba

The main objective of Radio Frequency Identification (RFID) systems is to provide fast identification for tagged objects. The noteworthy advantage of RFID technology, is its capability to identify objects simultaneously. However, when radio frequency signals are emitted simultaneously, there is a probability of collision occurrence, and thereby resulting in a waste of resources. Collision is a time-consuming event that reduces the performance of RFID systems. Consequently, several anti-collision algorithms have been proposed in the literature, attempt to decrease the collision occurrence probability. In almost all the existing anti-collision algorithms, a prior knowledge of the number of tags has a significant …

Sptf: Smart Photo-Tagging Framework On Smart Phones, Hao Xu, Hong-Ning Dai, Walter Hon-Wai Lau

Hong-Ning Dai

Smart phones, as one of the most important platforms for personal communications and mobile computing, have evolved with various embedded devices, such as cameras, Wi-Fi transceivers, Bluetooth transceivers and sensors. Specifically, the photos taken by a smart phone has the approximate or even equivalent image quality to that of a professional camera. As a result, smart phones have become the first choice for people to take photos to record their ordinary life. However, how to manage thousands of photos on a smart phone becomes a challenge. In this paper, we propose a new architecture in terms of Smart Photo-Tagging Framework …

Estimation Of High-Dimensional Brain Connectivity From Fmri Data Using Factor Modeling, Chee-Ming Ting Phd, Abd-Krim Seghouane, Sh-Hussain Salleh, Alias M. Noor

Chee-Ming Ting

We consider identifying effective connectivity of brain networks from fMRI time series. The standard vector autoregressive (VAR) models fail to give reliable network estimates, typically involving very large number of nodes. This paper adopts a dimensionality reduction approach based on factor modeling, to enable effective and efficient high-dimensional VAR analysis of large network connectivity. We derive a subspace VAR (SVAR) model from the factor model (FM) in which the observations are driven by a lower dimensional subspace of common latent factors, following an autoregressive dynamics. We consider the principal components (PC) method which can produce consistent estimators for the FM, …

Estimating Dynamic Cortical Connectivity From Motor Imagery Eeg Using Kalman Smoother & Em Algorithm, S. Balqis Samdin, Chee-Ming Ting Phd, Sh-Hussain Salleh, Mahyar Hamedi, Alias Mohd Noor

Chee-Ming Ting

This paper considers identifying effective cortical connectivity from scalp EEG. Recent studies use time-varying multivariate autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve the KF estimates, and the expectation-maximization (EM) algorithm to infer the unknown model parameters from EEG. We also consider solving the volume conduction problem by modeling the induced instantaneous correlations using a full noise covariate. Simulation results show the superiority of KS in tracking the coefficient changes. We …

Introduction To Rfid Technology And Collision Problem, Masoud Shakiba

Masoud Shakiba

Radio Frequency Identification (RFID) system is a new communication technology to identify the objects using electromagnetic waves. The main superiority of RFID systems in comparison with other identification systems, such as barcodes, is its ability in simultaneous identification process, but when more than one tag wants to communicate with the reader, collision problem occurs and wastes time and increases energy consumption. This reduces efficiency of the identification process in RFID systems. Collision problem happens when the reader has to identify more than one tag at the same time. Consequently, it is essential to develop an efficient anti-collision algorithm to save …

Artifact Removal From Single-Trial Erps Using Non-Gaussian Stochastic Volatility Models And Particle Filter, Chee-Ming Ting Phd, Sh-Hussain Salleh, Z. M. Zainuddin, Arifah Bahar

Chee-Ming Ting

This paper considers improved modeling of artifactual noise for denoising of single-trial event-related potentials (ERPs) by state-space approach. Instead of the inadequate constant variance models used in existing studies, we propose to use stochastic volatility (SV) models to better describe the time-varying volatility in real ERP noise sources. We further propose a class of non-Gaussian SV models to capture the abrupt volatility changes typically present in impulsive noise, to improve artifact removal from ERPs. Two specifications are considered: (1) volatility driven by a heavy-tailed component and (2) transformation of volatility. Both result in volatility processes with heavy-tailed transition densities which …

Computer Aided Pronunciation Learning For Al-Jabari Method: A Review, Noor Jamaliah Ibrahim, Mohd Yamani Idna Idris, Zulkifli Mohd Yusoff

Noor Jamaliah Ibrahim

Speech processing for Quranic Arabic has been carried out, and has been such an active field of research, since a few years ago. We propose in this paper, a review that focuses on the use and on the potential of automatic speech recognition (ASR) computer-based technology to supporting Quranic learning processes. Thus, the aspects of Computer-Aided Pronunciation Learning (CAPL) will be discussed, focus towards the implementation on Quranic learning of Al-Jabari method. We believe this method is a fast, efficient method of learning, as well as a practical way of learning Al-Quran using ICT tools. The advantages and drawbacks of …

Removing Milky Way From Airglow Images Using Principle Component Analysis, Zhenhua Li, Alan Z. Liu, Gulamabas G. Sivjee

Alan Z Liu