User behaviour monitoring using mobile phones to improve 5G services and performance

Abstract 4G has been widely commercialised, and 5G is currently under development. The expected data bandwidth for 5G is 100 times faster than 4G and 500 times faster than 3G; however, the evolution of telecommunication technologies involves both a boost in speed and the enhancement of user experience. The key word used to describe 5G is ‘user-centric’, rather than ‘service-centric’ for 4G, and thus user behaviours of mobile data usage should be further investigated. On the other hand, the testing equipment currently being used for base stations is limited to hardware devices, such as spectrum analysers and power meters. These testing methods do not include the considerable potential variations in data demands due to changes in user behaviours, which could be resolved by presuming that all data resources could be dynamically allocated by real-time events. A complete system has been designed and implemented in this study to investigate current user behaviours regarding mobile data usage. The system consists of three individual parts, including a user iOS application, a web server and an administrative iOS application. Ten devices were tested within the two-month data collection period. Although the sample size was too small to produce any statistical results, it was found that data usage behaviours differ from user to user, with the exception of using more than 10 times the Wi-Fi over WWAN data at all times. The data also proved that some of the usage case families, which are described in the NGMN 5G white paper, do have strong demands, which could not be fulfilled using current telecommunication technologies due to technological gaps. This paper shows that the system proposed is a feasible method to investigate user behaviours of mobile data usage. If the sample size of users involved could be increased in the future, it would be possible to develop a model for real-time simulations of mobile users in specific areas so that limited connection resources could be dynamically allocated. Moreover, the basic communication infra-structures, such as base stations, should be well-planned and developed in advance to fulfill the potential 5G demand.</jats:p

Object Detection and Localization in 2D & 3D Environment

University of Technology Sydney. Faculty of Engineering and Information Technology.Computer vision is a science that studies how to make machines "see." It refers to utilizing vision sensors and computers to identify, locate, and track objects. Under this topic, this thesis proposed three frameworks to improve 2D and 3D object detection and localization performance. In the first 3D object detection framework, we investigated the bilateral convolution layers’ feasibility to alternate the widely used point cloud voxelization process. The second framework explored the voxel-wise and point-wise proposal fusions method to improve 3D object detection performance. For the 2D instance segmentation, the framework formed an NMS-free and anchor-free detector designed explicitly for the eye-to-hand robotic system. In existing works, most of the state-of-the-art 3D object detection approaches are based on the point clouds’ voxelization method to sample the point cloud into a subdivide voxel space. Although it provides an efficient way to process point cloud data, its lack of feature relationship on voxel-level limits the model’s detection accuracy. Furthermore, the voxel sizes hyperparameters tuning increased the model complexity, resulting in a fluctuated model performance. To this end, we aim to simplify the process by re-projecting the point cloud data onto a lattice hyper-plane that saves point cloud processing time while maintaining the model accuracy. The proposed framework Bilateral Lattice Point Network (BLPNet) is provided in chapter three. In the second framework, Point and Voxel Fusion Net (PVF-Net) is proposed to further push the 3D object detection performance forward. In two-stage approaches, increasing the first stage proposals recall rate positively influences the model final prediction performance. Therefore, in the PVF-Net, we proposed a twofold proposal fusion architecture to extract and fuse the voxel-level and point-level features of the point clouds. The model details are in chapter four, mainly consisting of two novel modules: the Twofold Proposal Fusion (TPF) module and the ROI Deep Fusion (RDF) module. Lastly, it is well-known that 3D and 2D sensors jointly depict the real world. In chapter five, 2D object detection will become the next goal for improvement. So far, the existing 2D instance segmentation algorithms developed significantly and reached a saturated performance. However, there is no solid solution for heavy occluded or diagonally arranged objects, especially in the vision-guided robot picking system. To solve the problem above, we proposed a real-time occlusion and oblique friendly instance segmentation framework, terms as Keypoint-Mask, assisting the robotic system to handle the complicated detection scenario

The TP53-Related Signature Predicts Immune Cell Infiltration, Therapeutic Response, and Prognosis in Patients With Esophageal Carcinoma

TP53 mutation (TP53MUT) is one of the most common gene mutations and frequently occurs in many cancers, especially esophageal carcinoma (ESCA), and it correlates with clinical prognostic outcomes. Nevertheless, the mechanisms by which TP53MUT regulates the correlation between ESCA and prognosis have not been sufficiently studied. Here, in the current research, we constructed a TP53MUT-related signature to predict the prognosis of patients with esophageal cancer and successfully verified this model in patients in the TP53 mutant group, esophageal squamous cell carcinoma group, and adenocarcinoma group. The risk scores proved to be better independent prognostic factors than clinical features, and prognostic features were combined with other clinical features to establish a convincing nomogram to predict overall survival from 1 to 3 years. In addition, we further predicted the tumor immune cell infiltration, chemical drugs, and immunotherapy responses between the high-risk group and low risk group. Finally, the gene expression of the seven-gene signature (AP002478.1, BHLHA15, FFAR2, IGFBP1, KCTD8, PHYHD1, and SLC26A9) can provide personalized prognosis prediction and insights into new treatments

CO2 saturated brine injected into fractured shale: An X-ray micro-tomography in-situ analysis at reservoir conditions

Fracture morphology and permeability are key factors in enhanced gas recovery (EOR) and Carbon Geo-storage (CCS) in shale gas reservoirs as they determine production and injection rates. However, the exact effect of CO2-saturated (live) brine on shale fracture morphology, and how the permeability changes during live brine injection and exposure is only poorly understood. We thus imaged fractured shale samples before and after live brine injection in-situ at high resolution in 3D via X-ray micro-computed tomography. Clearly, the fractures’ aperture and connectivity increased after live brine injection

Physiological signal acquisition system for wireless communication

Empirical thesis.Bibliography: pages 67-69.Introduction -- Background -- Communication between the CC3200 LaunchPad and the computer -- DSP, signal display and GUI -- Wireless HEARLab System prototype -- Real-time wireless HEARLab System prototype demonstration -- Result -- Conclusion -- Abbreviations -- Bibliography -- Appendices.National Acoustic Laboratory uses HEARLab System to record to record the brain function of hearing loss people. Typically, the principle of the HEARLab System is to detect the hearing loss level of the patient by recording the patient's electroencephalography (EEG). Recently, because of the wired signal transmission method, the patient has to be constrained in a particular space, which causes inconvenience to the experimenter and the subject. Thus, the aim of this project is to use a wireless connection method to replace the traditional physical line connection method and to achieve the Internet of things (IoT). In this thesis, Texas Instruments CC3200 LaunchPad is to be used to make the wireless information transmission. Therefore, based on the Wi-Fi environment the new HEARLab System can wirelessly transmit information between the PC and the CC3200 LaunchPad by using User Datagram Protocol (UDP). The UDP is written in Java and discussed in detail in the thesis. Further work aims to process the received EEG signals. Typically, regarding the signal processing principle, the received EEG signals are stored in digital format on the computer. On this occasion Java digital signal processing is going to be introduced in the thesis. All the related code or tools will be attached in Dropbox.Mode of access: World wide web.1 online resource (vii, 77 pages colour illustrations

Mechanical couplings of 3D lattice materials discovered by micropolar elasticity and geometric symmetry

Similar to Poisson's effect, mechanical coupling is a directional indirect response by a directional input loading. With the advance in manufacturing techniques of 3D complex geometry, architected materials with unit cells of finite volume rather than a point yield more degrees of freedom and foster exotic mechanical couplings such as axial-shear, axial-rotation, axial-bending, and axial-twisting. However, most structural materials have been built by the ad hoc design of mechanical couplings without theoretical support of elasticity, which does not provide general guidelines for mechanical couplings. Moreover, no comprehensive study of all the mechanical couplings of 3D lattices with symmetry operations has been undertaken. Therefore, we construct the decoupled micropolar elasticity tensor of 3D lattices to identify individual mechanical couplings correlated with the point groups. The decoupled micropolar elasticity tensors, classified with 32 point groups, provide 15 mechanical couplings for 3D lattices. Our findings help provide solid theoretical guidelines for the mechanical couplings of 3D structural materials with potential applications in various areas, including active metamaterials, sensors, actuators, elastic waveguides, and acoustics

Mitigation of Ice-Induced Vibration of Offshore Platform Based on Gated Recurrent Neural Network

Ice-induced vibration is one of the major risks that face the offshore platform located in cold regions. In this paper, the gated recurrent neural network (GRNN) is utilized to predict and suppress the response of offshore platforms subjected to ice load. First, a simplified model of the offshore platform is derived and validated based on the finite element model (FEM). The time history of the floating ice load is generated using the harmonic superposition method. Gated Recurrent Unit Network (GRU) and the Long-Short-Term Memory Network (LSTM) are composed in MATLAB to predict the behavior of the off-shore platform. Afterward, the linear quadratic regulator (LQR) control algorithm is used to calculate the controlling force for the training of the GRU/LSTM-based prediction controller. Numerical results show that the ice-induced vibration response prediction method based on GRU network design can predict the structural response with satisfying accuracy, and the ice-induced vibration response control method based on the LSTM network and GRU network design can learn the LQR method well and achieve good control effect. Time lag and other problems that the vibration control programs often encountered were solved well

Electrochemical characterization of electrolyte purity for CO2 reduction studies

In this work, we put forward a simple electrochemical approach to conveniently determine the purity of the electrolyte solution which improves the reproducibility and reliability of electrochemical CO2 reduction data such that experimental outcomes can be readily compared across research groups. The method uses a polycrystalline Au electrode as a probe to detect electrolyte impurities that can readily be adsorbed to surfaces even when ultrahigh-purity (99.999%) commercial chemicals are used as received. Herein, we show how the extent of trace contamination alters the measured activity during CO2 reduction and demonstrate electrochemical methods to capture these trace contaminants, such that CO2 reduction activity can be reproduced reliably