Multi-Task Learning Approach for Natural Images' Quality Assessment

Blind image quality assessment (BIQA) is a method to predict the quality of a natural image without the presence of a reference image. Current BIQA models typically learn their prediction separately for different image distortions, ignoring the relationship between the learning tasks. As a result, a BIQA model may has great prediction performance for natural images affected by one particular type of distortion but is less effective when tested on others. In this paper, we propose to address this limitation by training our BIQA model simultaneously under different distortion conditions using multi-task learning (MTL) technique. Given a set of training images, our Multi-Task Learning based Image Quality assessment (MTL-IQ) model first extracts spatial domain BIQA features. The features are then used as an input to a trace-norm regularisation based MTL framework to learn prediction models for different distortion classes simultaneously. For a test image of a known distortion, MTL-IQ selects a specific trained model to predict the image’s quality score. For a test image of an unknown distortion, MTLIQ first estimates the amount of each distortion present in the image using a support vector classifier. The probability estimates are then used to weigh the image prediction scores from different trained models. The weighted scores are then pooled to obtain the final image quality score. Experimental results on standard image quality assessment (IQA) databases show that MTL-IQ is highly correlated with human perceptual measures of image quality. It also obtained higher prediction performance in both overall and individual distortion cases compared to current BIQA models

Eye Closure and Open Detection Using Adaptive Thresholding Histogram Enhancement (ATHE) Technique and Connected Components Utilisation

Eye closure detection is an important operation prior to carry out the main algorithm such as iris recognition algorithms, and eye tracking algorithms. This paper introduces a method to detect eye closure using Adaptive Thresholding Histogram Enhancement (ATHE) technique and connected component utilisation. The ATHE technique is a combination of histogram enhancement and estimation threshold technique. Firstly, in this proposed method the eye region is required to be localised. The ATHE technique enhances the eye region image then and yield the threshold value to segment the iris region. Based on the segmentation result, the connected components of binary image are used to classify the state of eye whether open or close. This classification is based on the shape and size of segmented region. The performance of the proposed technique is tested and validated by using UBIRIS, MMU and CASIA iris image database

Indoor Location Estimation Utilizing Wi-Fi Signals

Global Positioning System is commonly been used for locating a position of a specific structure in finding geographical coordinates of a target area. Though, this application is still having a restricted in term of the signals, might not well operated and ineffective for indoor usage. The study aim is to develop positioning and localization systems by using Wi-Fi signal. Estimation was made based on the measurement of wireless distance for estimation the user’s coordinates. Analysis of views called the fingerprint algorithm is used in this study. The algorithm involved two phases over an offline and the online phases of the survey. Unidentified user’s coordinates will be in the online phase by comparative databases collected in the survey phase. MATLAB Graphical User Interface and Android has been used to develop a user interface for simulation purposes. Several analyses were performed to define the precision and efficiency of occurred error as the number of access points and the traffic environment. Finally, the user required to provide several inputs e.g. the exact location and the RSS from AP’s number at the present location. The simulation-based software will evaluate the estimation location and positioning of the user and will match to user’s precise locatio

Blind Source Separation On Biomedical Field By Using Nonnegative Matrix Factorization

The study of separating heart from lung sound has been investigated and researched for years. However, a novel approach based on nonnegative matrix factorization (NMF) as a skill of blind source separation (BSS) that utilized in biomedical field is fresh presented. Lung sound gives beneficial information regarding lung status through respiratory analysis. However, interrupt of heart sound is the obstacle from taking precise and exact information during respiratory analysis. Thus, separation heart sound from lung sound is a way to overcome this issue in order to determine the accuracy of respiratory analysis. This paper proposes factorizations approach that concern on the 2 dimensional which is combination of frequency domain and time domain or well known as NMF2D. The proposed method is developed under the divergence of Least Square Error and Kullback-Leibler and it demonstrates from a single channel source. In this paper, we will forms a multivariate data and it will proceed for dimension reduction by log frequency domain. Experimental tests and comparisons will be made via different divergence to verify and evaluate efficiency of the proposed method in term performance measurement

A new approach to highway lane detection by using hough transform technique

This paper presents the development of a road lane detection algorithm using image processing techniques.This algorithm is developed based on dynamic videos, which are recorded using on-board cameras installed in vehicles for Malaysian highway conditions.The recorded videos are dynamic scenes of the background and the foreground, in which the detection of the objects, presence on the road area such as vehicles and road signs are more challenging caused by interference from background elements such as buildings, trees, road dividers and other related elements or objects. Thus, this algorithm aims to detect the road lanes for three significant parameter operations; vanishing point detection, road width measurements, and Region of Interest (ROI) of the road area, for detection purposes.The techniques used in the algorithm are image enhancement and edges extraction by Sobel filter, and the main technique for lane detection is a Hough Transform. The performance of the algorithm is tested and validated by using three videos of highway scenes in Malaysia with normal weather conditions, raining and a night-time scene, and an additional scene of a sunny rural road area. The video frame rate is 30fps with dimensions of 720p (1280x720) HD pixels. In the final achievement analysis, the test result shows a true positive rate, a TP lane detection average rate of 0.925 and the capability to be used in the final application implementation

High-Performance, Fault-Tolerant Architecture for Reliable Hybrid Nanolectronic Memories

Although hybrid nanoelectronic memories (hybrid memories) promise scalability potentials such as ultrascale density and low power consumption, they are expected to suffer from high defect/fault density reducing their reliability. Such defects/faults can impact any part of the memory system including the memory cell array, the encoder and the decoder. This article presents a high-performance, fault-tolerant architecture for hybrid memories; it is based on a combination of two techniques: (i) an error correction scheme that tolerates both random and clustered faults in memory cell array and (ii) an on-line masking incorporated into the decoder to tolerate faults in the decoder. Moreover, the decoding process is optimized for area and performance by reversing the decoding sequence. Experimental results show that the proposed architecture realizes a higher performance and competitive reliability level at a comparable overhead as compared with the state-of-the-art. For example, the architecture decodes 5× faster and provides 0.7% better reliability (assuming 10% fault rate) at the cost of similar area overhead (for 1024-bit memory word) as compared to Reed-Solomon code

Development Of Wireless Power Transfer Using Capacitive Method For Mouse Charging Application

Wireless power transfer (WPT) is a non-contact power transfer within a distance. With the advantage of not-contact concept, WPT enhances the flexibility movement of the devices. Basically, there are three types of the WPT which are inductive power transfer (IPT), capacitive power transfer (CPT) and acoustic power transfer (APT). Among these, capacitive power transfer (CPT) has the advantages of confining electric field between coupled plates, metal penetration ability and also the simplicity in circuit topologies. Therefore, we focus on the capacitive method in this paper. To be specific, this paper aims to develop a wireless mouse charging system using capacitive based method. This method enables wireless power transmission from mouse pad to a wireless mouse. Hence, no battery requires to power up the mouse. In this paper, a high efficiency Class-E converter is described in details to convert the DC source to AC and the compensation circuit of resonant tank is also proposed at the transmitter side in order to improve the efficiency. In the end, a prototype is developed to prove the developed method. The performances analysis of the developed prototype is discussed and the future recommendation of this technique is also presented

Analysis of indoor location and positioning via Wi-Fi signals at FKEKK,UTeM

Global Positioning System (GPS) is widely used as public location and positioning system in navigation of user. However, this application signal is limited, not operational, not available and inefficient for indoor environment. Therefore, the purpose of this project is to analyze indoor location and positioning utilizing the Wi-Fi signals. Wireless position estimation is based on distance measurement in determining the coordinates of user. Scene analysis which called fingerprinting technique is implemented in this project with measurement of received signal strength (RSS) from all the access point. The project has two phases to go through which are the surveying phase and online phase. The unknown location of the target user RSS during online phase will be compare to the database that has been collected during surveying phase. The Euclidean distance algorithm is implemented in calculating the location estimation which the MATLAB software used as the simulation tools. Several analyses have been done to determine the accuracy and effectiveness of the positioning error due to numbers of access point and environment traffic condition