The use of a low-cost MEMS-based Inertial Measurement Unit (IMU) provides a cost-effective approach for navigation purposes. Foot-mounted IMU is a popular option for indoor inertial pedestrian navigation, as a small and light MEMS-based inertial sensor can be tied to a pedestrian's foot or shoe. Without relying on GNSS or other external sensors to enhance navigation, the foot-mounted pedestrian navigation system can autonomously navigate, relying solely on the IMU. This is typically performed with the standard strapdown navigation algorithm in a Kalman filter, where Zero Velocity Updates (ZVU) are used together to restrict the error growth of the low-cost inertial sensors. ZVU is applied every time the user takes a step since there exists a zero velocity condition during stance phase. While velocity and correlated attitude errors can be estimated correctly using ZVUs, heading error is not because it is unobservable. In this paper, we extend our previous work to correct the heading error by aiding it using Multiple Polygon Areas (MPA) with adaptive weighting factor. We termed the approach as Adaptive Cardinal Heading Aided Inertial Navigation (A-CHAIN). We formulated an adaptive weighting factor applied to measurement noise to enhance measurement confidence. We then incorporated MPA heading into the algorithm, whereas multiple buildings with the same orientation are grouped together and assigned a specific heading information as a priori. Results shown that against the original CHAIN, the proposed Adaptive-CHAIN improved the position accuracy by more than five-fold.

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0·1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system.

This paper presents a Dynamic Gradient Pattern (DGP) based on Kalman filtering technique for urban road users tracking. DGP technique is proposed to enhance rigid object descriptive ability for improved verification. DGP descriptor along with weighted centroid was integrated with a Kalman filtering framework to enhance data association robustness and tracking accuracy. To handle multiple objects tracking, a DGP verification approach is addressed based on normalized Bhattacharyya distance. The proposed technique achieves a closer trajectory for rigid body movement. The DGP descriptor can discriminate the objects correctly, and it overcomes the partial occlusion and misdetection by verifying object location using the normalized Bhattacharyya distance between DGP features. Experimental evaluation is performed on urban videos that include a slow-motion temporary stop and partial occlusion. The experimental results demonstrate that the detecting and tracking accuracy are above 98.08% and 97.70% respectively.

Motion segmentation is a fundamental step in urban traffic surveillance systems, since it provides necessary information for further processing. Background subtraction techniques are widely used to identify foreground moving vehicles from static background scene. Conventional techniques utilize single background model or Gaussian mixture model, which involves either poor adaptation or high computation.The complexity of urban traffic scenarios lies in pose and orientation variations, slow or temporarily stopped vehicles and sudden illumination variations. To address these problems Sigma-Delta Mixture Model (SDMM)is proposed. Mixed distributions are updated dynamically based on matching and contribution in the two order temporal statistics. The constant amplification factor is replaced byweightedfactor to update the variance rate over its temporal activity. The proposed technique achieve robust and accurate performance,which improves adaptation capability with balanced sensitivity and reliability, moreover, integerlinear operations enables the real-time capability.

Graduate marketability has become a critical issue as many graduates have problem in getting employed after completing their study in the university. This is mainly due to their inability to showcase their achievements and market their skills properly to prospective employers. This study firstly identifies the user requirements from all identified stakeholders, and secondly, based on these findings, develops a portal prototype that incorporates the integrated features called the Integrated Graduate Portal (IGP). Finally, the study evaluates the use of the IGP in facilitating graduate marketability. The aims of this portal are to facilitate graduate marketability by showcasing graduates’ contents and skills using electronic portfolio (e-portfolio), to employers, and to reduce the communication gap between the employers, graduates and their university. Two phases of survey were conducted for this study. The first one was conducted to identify and gather the user requirements from all identified stakeholders (graduates, employers, lecturers and administrator). These requirements were analyzed and carefully selected prior to incorporating them during the development of the portal. Once the IGP prototype was developed, the second survey was conducted to evaluate the use of IGP to facilitate graduate marketability. The evaluation results showed that the IGP may help to facilitate graduate marketability. It was found that integration features such as graduates’ e-portfolios, employers’ job requirements information, verification of graduates’ content by lecturers, and collaboration of graduates, employers and university in the IGP, are features that may aid graduates to facilitate their marketability. In conclusion, it is hoped that the use of the IGP may help the graduates to facilitate their marketability and bridge the communication gap between the graduates, employers, lecturers and university, therefore improving the interaction and collaboration between all stakeholders. Index terms- Electronic portfolio (e-portfolio), graduate marketability, Integrated Graduate Portal (IGP).

Outdoor images that are captured in bad weather conditions have low contrast and infidelity colours. Under the turbid medium conditions such as haze, mist, fog and drizzle, the light which reaches to the sensor is attenuated by atmospheric particles. These atmospheric phenomena degrade the contrast intensity of outdoor images based on haze density. In this research, we present new method to improve both the intensity and fine details of outdoor scene images. The RGB (Red, Green and Blue) input image is converted to the HSI (Hue Saturation Intensity) colour space and the density of the haze is estimated. Then, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance the degraded intensity based on the estimation of the density of the haze. Our method is effective in a wide range of weather conditions and under different levels of visibility. � 2016 Penerbit UTM Press. All rights reserved

A new Zero Velocity Interval (ZVI) detector is proposed and investigated in this article to augment the computation of a pedestrian’s position. In a low-cost pedestrian navigation system, the position of a pedestrian can be computed by using inertial navigation algorithms. The low-cost pedestrian navigation system employs an inertial measurement unit (IMU) comprised of accelerometer and gyroscope sensors to record acceleration and attitude rate. These measurements, when integrated mathematically, yield velocity and position. Similarly, attitude rate changes to attitude. The algorithm will then be able to figure out the changes in pedestrian position with the proper attitude. However, due to its low-cost nature, these sensors are built in such a way that their measurements are easily corrupted by noise, and once integrated mathematically, the measurement error grows exponentially. Zero Velocity Update (ZUPT) algorithm is frequently used to limit these errors. It works by detecting the ZVI that occur when the foot is on the ground. Assuming that the foot on the ground should have zero velocity, any remaining velocity measurements detected during this period are considered an error and are fed back to the navigation algorithm for correction. To detect the ZVI, a few commonly used detectors, namely Angular Rate Energy (ARE), Acceleration Moving Variance (MV), Acceleration Magnitude (MAG), and Generalized Likehood Ratio Test (GLRT) were revisited. These detectors were tested with real-world datasets of walking pedestrians. Then, a new detector, the Angular Rate Moving Variance (ARMV) detector, is proposed, and its performance is compared to that of the existing detectors.

Objectives: As a result of signal transmission in WiMAX too long distance, and because there are many request packets arrive at WiMAX gateway request handovers, this cost a congestion which may result in loss or delay in packet time sensitive. The objectives of this paper are to investigate the delay when packets with real time sensitive handover in WiMAX gateway and to measure and enhance the handover performance. Methods/Statistical Analysis: To overcome this problem, and achieve the objectives is propose a new scheme Resource Reservation Protocol & Random Early Detection with Weighted Fair Queuing (RR-WFQ) in order to analyze and enhance delay for real time packet in WiMAX gateway. This schema applies all Resource Reservation Protocol (RSVP), Random Early Detection (RED), and Weighted Fair Queuing (WFQ) as a Scheduling, RED as a mechanism and RSVP as a signaling protocol. Findings: The proposed scheme enhances the QoS by minimizing the delay in WiMAX gateway and BS. The scheme enhances the delay in all queues in a gateway, and video conferencing. The result of the scheme shows an enhancement delay in the WiMAX gateway. Application/ Improvements: Mathematical analysis and modeling have been done using queuing theory. Simulation has been done using OPNET simulation. Simulation results for the new approach confirms that has minimized delay in WiMAX gateway and BS the gateway and enhances the QoS.

Zero Velocity Update (ZUPT) is an important update to aid an autonomous inertial pedestrian navigation. The objectives of this paper are to briefly revisit the concept of ZUPT and its importance, testing it on real walking pedestrian and comparing its performance when used with either conventional ‘Dead Reckoning approach (DR)’ or with ‘Kalman Filter approach (KF)’ as either one of these approaches is commonly used in literature. Performances were analyzed further with the inclusion of two correction modes (Linearly Weighted Interpolation and Residual Velocity). Experiments were performed using a low cost Inerital Measurement Unit (IMU) from MicroStrain (3DM-GX1). It was shown that the KF approach outperformed DRonly approach, but comparable performance with KF was noticed when DR is combined with correction mode. Finally, a combination of RV correction mode with forward KF solution was shown to improve the position output