Novel channel tracking and equalization methods in MU-MIMO-OFDM systems

The methodology undertaken, the channel model and the system model created for developing a novel adaptive equalization method and a novel channel tracking method for uplink of MU-MIMO-OFDM systems is presented in this paper. The results show that the channel tracking method works with 97% accuracy, while the training-based initial channel estimation method shows poor performance in estimating the actual channel comparatively.

Hearing loss and a supportive tactile signal in a navigation system : effects on driving behavior and eye movements

An on-road study was conducted to evaluate a complementary tactile navigation signal on driving behaviour and eye movements for drivers with hearing loss (HL) compared to drivers with normal hearing (NH). 32 participants (16 HL and 16 NH) performed two preprogrammed navigation tasks. In one, participants received only visual information, while the other also included a vibration in the seat to guide them in the correct direction. SMI glasses were used for eye tracking, recording the point of gaze within the scene. Analysis was performed on predefined regions. A questionnaire examined participant's experience of the navigation systems. Hearing loss was associated with lower speed, higher satisfaction with the tactile signal and more glances in the rear view mirror. Additionally, tactile support led to less time spent viewing the navigation display.

Order tracking for discrete-random separation in variable speed conditions

The transmission path from the excitation to the measured vibration on the surface of a mechanical system introduces a distortion both in amplitude and in phase. Moreover, in variable speed conditions, the amplification/attenuation and the phase shift, due to the transfer function of the mechanical system, varies in time. This phenomenon reduces the effectiveness of the traditionally tachometer based order tracking, compromising the results of a discrete-random separation performed by a synchronous averaging. In this paper, for the first time, the extent of the distortion is identified both in the time domain and in the order spectrum of the signal, highlighting the consequences for the diagnostics of rotating machinery. A particular focus is given to gears, providing some indications on how to take advantage of the quantification of the disturbance to better tune the techniques developed for the compensation of the distortion. The full theoretical analysis is presented and the results are applied to an experimental case.

Tracking spatio temporal movement of human in terms of space utilization using media access control address data

Using Media-Access-Control (MAC) address for data collection and tracking is a capable and cost effective approach as the traditional ways such as surveys and video surveillance have numerous drawbacks and limitations. Positioning cell-phones by Global System for Mobile communication was considered an attack on people's privacy. MAC addresses just keep a unique log of a WiFi or Bluetooth enabled device for connecting to another device that has not potential privacy infringements. This paper presents the use of MAC address data collection approach for analysis of spatio-temporal dynamics of human in terms of shared space utilization. This paper firstly discuses the critical challenges and key benefits of MAC address data as a tracking technology for monitoring human movement. Here, proximity-based MAC address tracking is postulated as an effective methodology for analysing the complex spatio-temporal dynamics of human movements at shared zones such as lounge and office areas. A case study of university staff lounge area is described in detail and results indicates a significant added value of the methodology for human movement tracking. By analysis of MAC address data in the study area, clear statistics such as staff’s utilisation frequency, utilisation peak periods, and staff time spent is obtained. The analyses also reveal staff’s socialising profiles in terms of group and solo gathering. The paper is concluded with a discussion on why MAC address tracking offers significant advantages for tracking human behaviour in terms of shared space utilisation with respect to other and more prominent technologies, and outlines some of its remaining deficiencies.

Object tracking via non-Euclidean geometry : a Grassmann approach

A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the abovementioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.

Design of a robust grid interface system for PMSG-based wind turbine generators

A robust and reliable grid power interface system for wind turbines using a permanent-magnet synchronous generator (PMSG) is proposed in this paper, where an integration of a generator-side three-switch buck-type rectifier and a grid-side Z-source inverter is employed as a bridge between the generator and the grid. The modulation strategy for the proposed topology is developed from space-vector modulation and Z-source network operation principles. Two PMSG control methods, namely, unity-power-factor control and rotor-flux-orientation control (Id = 0), are studied to establish an optimized control scheme for the generator-side three-switch buck-type rectifier. The system control scheme decouples active- and reactive-power control through voltage-oriented control and optimizes PMSG control for the grid- and generator-side converters independently. Maximum power point tracking is implemented by adjusting the shoot-through duty cycles of the Z-source network. The design considerations of the passive components are also provided. The performances and practicalities of the designed architecture have been verified by simulations and experiments.

Dual inverter based battery energy storage system for grid connected photovoltaic systems

This paper explores a new breed of energy storage system interfacing for grid connected photovoltaic (PV) systems. The proposed system uses the popular dual inverter topology in which one inverter is supplied by a PV cell array and the other by a Battery Energy Storage System (BESS). The resulting conversion structure is controlled in a way that both demand matching and maximum power point tracking of the PV cell array are performed simultaneously. This dual inverter topology can produces 2, 3, 4 and 5 level inverter voltage waveforms at the dc-link voltage ratios of 0:1, 1:1, 2:1 and 3:2 respectively. Since the output voltage of the PV cell array and the battery are uncorrelated and dynamically change, the resulting dc-link voltage ratio can take non-integer values as well. These noninteger dc-link voltage ratios produce unevenly distributed space vectors. Therefore, the main issue with the proposed system is the generation of undistorted current even in the presence of unevenly distributed and dynamically changing space vectors. A modified space vector modulation method is proposed in this paper to address this issue and its efficacy is proved by simulation results. The ability of the proposed system to act as an active power source is also verified.

Data fusion and tracking with multiple UAVs

This chapter describes decentralized data fusion algorithms for a team of multiple autonomous platforms. Decentralized data fusion (DDF) provides a useful basis with which to build upon for cooperative information gathering tasks for robotic teams operating in outdoor environments. Through the DDF algorithms, each platform can maintain a consistent global solution from which decisions may then be made. Comparisons will be made between the implementation of DDF using two probabilistic representations. The first, Gaussian estimates and the second Gaussian mixtures are compared using a common data set. The overall system design is detailed, providing insight into the overall complexity of implementing a robust DDF system for use in information gathering tasks in outdoor UAV applications.

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

Partial shading and rapidly changing irradiance conditions significantly impact on the performance of photovoltaic (PV) systems. These impacts are particularly severe in tropical regions where the climatic conditions result in very large and rapid changes in irradiance. In this paper, a hybrid maximum power point (MPP) tracking (MPPT) technique for PV systems operating under partially shaded conditions witapid irradiance change is proposed. It combines a conventional MPPT and an artificial neural network (ANN)-based MPPT. A low cost method is proposed to predict the global MPP region when expensive irradiance sensors are not available or are not justifiable for cost reasons. It samples the operating point on the stairs of I–V curve and uses a combination of the measured current value at each stair to predict the global MPP region. The conventional MPPT is then used to search within the classified region to get the global MPP. The effectiveness of the proposed MPPT is demonstrated using both simulations and an experimental setup. Experimental comparisons with four existing MPPTs are performed. The results show that the proposed MPPT produces more energy than the other techniques and can effectively track the global MPP with a fast tracking speed under various shading patterns.

Modeling kinect sensor noise for improved 3D reconstruction and tracking

We contribute an empirically derived noise model for the Kinect sensor. We systematically measure both lateral and axial noise distributions, as a function of both distance and angle of the Kinect to an observed surface. The derived noise model can be used to filter Kinect depth maps for a variety of applications. Our second contribution applies our derived noise model to the KinectFusion system to extend filtering, volumetric fusion, and pose estimation within the pipeline. Qualitative results show our method allows reconstruction of finer details and the ability to reconstruct smaller objects and thinner surfaces. Quantitative results also show our method improves pose estimation accuracy. © 2012 IEEE.

Tracking control of a class of Hamiltonian mechanical systems with disturbances

This paper presents a trajectory-tracking control strategy for a class of mechanical systems in Hamiltonian form. The class is characterised by a simplectic interconnection arising from the use of generalised coordinates and full actuation. The tracking error dynamic is modelled as a port-Hamiltonian Systems (PHS). The control action is designed to take the error dynamics into a desired closed-loop PHS characterised by a constant mass matrix and a potential energy with a minimum at the origin. A transformation of the momentum and a feedback control is exploited to obtain a constant generalised mass matrix in closed loop. The stability of the close-loop system is shown using the close-loop Hamiltonian as a Lyapunov function. The paper also considers the addition of integral action to design a robust controller that ensures tracking in spite of disturbances. As a case study, the proposed control design methodology is applied to a fully actuated robotic manipulator.

Tracking locomotive position to evaluate sugarcane railway schedules and predict arrival time

This research utilised software developed for managing the Australian sugar industry's cane rail transport operations and GPS data used to track locomotives to ensure safe operation of the railway system to improve transport operations. As a result, time usage in the sugarcane railway can now be summarised and locomotive arrival time to sidings and mills can be predicted. This information will help the development of more efficient run schedules and enable mill staff and harvesters to better plan their shifts ahead, enabling cost reductions through better use of available time.

Chirp-spread-spectrum-based real time location system for construction safety management: A case study

Observing the working procedure of construction workers is an effective means of maintaining the safety performance of a construction project. It is also difficult to achieve due to a high worker-to-safety-officer ratio. There is an imminent need for the development of a tool to assist in the real-time monitoring of workers, in order to reduce the number of construction accidents. The development and application of a real time locating system (RTLS) based on the Chirp Spread Spectrum (CSS) technique is described in this paper for tracking the real-time position of workers on construction sites. Experiments and tests were carried out both on- and off-site to verify the accuracy of static and dynamic targets by the system, indicating an average error of within one metre. Experiments were also carried out to verify the ability of the system to identify workers’ unsafe behaviours. Wireless data transfer was used to simplify the deployment of the system. The system was deployed in a public residential construction project and proved to be quick and simple to use. The cost of the developed system is also reported to be reasonable (around 1800USD) in this study and is much cheaper than the cost of other RTLS. In addition, the CCS technique is shown to provide an economical solution with reasonable accuracy compared with other positioning systems, such as ultra wideband. The study verifies the potential of the CCS technique to provide an effective and economical aid in the improvement of safety management in the construction industry.

An Intelligent Driver Warning System

This chapter is focussed on the research and development of an intelligent driver warning system (IDWS) as a means to improve road safety and driving comfort. Two independent IDWS case studies are presented. The first study examines the methodology and implementation for attentive visual tracking and trajectory estimation for dynamic scene segmentation problems. In the second case study, the concept of driver modelling is evaluated which can be used to provide useful feedback to drivers. In both case studies, the quality of IDWS is largely determined by the modelling capability for estimating multiple vehicle trajectories and modelling driving behaviour. A class of modelling techniques based on neural-fuzzy systems, which exhibits provable learning and modelling capability, is proposed. For complex modelling problems where the curse of dimensionality becomes an issue, a network construction algorithm based on Adaptive Spline Modelling of Observation Data (ASMOD) is also proposed.

Robotic detection and tracking of Crown-Of-Thorns starfish

This paper presents a novel vision-based underwater robotic system for the identification and control of Crown-Of-Thorns starfish (COTS) in coral reef environments. COTS have been identified as one of the most significant threats to Australia's Great Barrier Reef. These starfish literally eat coral, impacting large areas of reef and the marine ecosystem that depends on it. Evidence has suggested that land-based nutrient runoff has accelerated recent outbreaks of COTS requiring extensive use of divers to manually inject biological agents into the starfish in an attempt to control population numbers. Facilitating this control program using robotics is the goal of our research. In this paper we introduce a vision-based COTS detection and tracking system based on a Random Forest Classifier (RFC) trained on images from underwater footage. To track COTS with a moving camera, we embed the RFC in a particle filter detector and tracker where the predicted class probability of the RFC is used as an observation probability to weight the particles, and we use a sparse optical flow estimation for the prediction step of the filter. The system is experimentally evaluated in a realistic laboratory setup using a robotic arm that moves a camera at different speeds and heights over a range of real-size images of COTS in a reef environment.