Enabling self organisation for future cellular networks.

The rapid growth in mobile communications due to the exponential demand for wireless access is causing the distribution and maintenance of cellular networks to become more complex, expensive and time consuming. Lately, extensive research and standardisation work has been focused on the novel paradigm of self-organising network (SON). SON is an automated technology that allows the planning, deployment, operation, optimisation and healing of the network to become faster and easier by reducing the human involvement in network operational tasks, while optimising the network coverage, capacity and quality of service. However, these SON autonomous features cannot be achieved with the current drive test coverage assessment approach due to its lack of automaticity which results in huge delays and cost. Minimization of drive test (MDT) has recently been standardized by 3GPP as a key self- organising network (SON) feature. MDT allows coverage to be estimated at the base station using user equipment (UE) measurement reports with the objective to eliminate the need for drive tests. However, most MDT based coverage estimation methods recently proposed in literature assume that UE position is known at the base station with 100% accuracy, an assumption that does not hold in reality. In this work, we develop a novel and accurate analytical model that allows the quantification of error in MDT based autonomous coverage estimation (ACE) as a function of error in UE as well as base station (user deployed cell) positioning. We first consider a circular cell with an omnidirectional antenna and then we use a three-sectored cell and see how the system is going to be affected by the UE and the base station (user deployed cell) geographical location information errors. Our model also allows characterization of error in ACE as function of standard deviation of shadowing in addition to the path-loss

Encrypted Web Traffic Classification Using Deep Learning

Traffic classification is essential in network management for operations ranging from capacity planning, performance monitoring, volumetry, and resource provisioning, to anomaly detection and security. Recently, it has become increasingly challenging with the widespread adoption of encryption in the Internet, e.g., as a de-facto in HTTP/2 and QUIC protocols. In the current state of encrypted traffic classification using Deep Learning (DL), we identify fundamental issues in the way it is typically approached. For instance, although complex DL models with millions of parameters are being used, these models implement a relatively simple logic based on certain header fields of the TLS handshake, limiting model robustness to future versions of encrypted protocols. Furthermore, encrypted traffic is often treated as any other raw input for DL, while crucial domain-specific considerations exist that are commonly ignored. In this thesis, we design a novel feature engineering approach that generalizes well for encrypted web protocols, and develop a neural network architecture based on Stacked Long Short-Term Memory (LSTM) layers and Convolutional Neural Networks (CNN) that works very well with our feature design. We evaluate our approach on a real-world traffic dataset from a major ISP and Mobile Network Operator. We achieve an accuracy of 95% in service-level classification with less raw traffic and smaller number of parameters, out-performing a state-of-the-art method by nearly 50% fewer false classifications. We show that our DL model generalizes for different classification objectives and encrypted web protocols. We also evaluate our approach on a public QUIC dataset with finer and application-level granularity in labeling, achieving an overall accuracy of 99%

Developing a Location-Based Recommender System Using Collaborative Filtering Technique in the Tourism Industry

The rapid growth of new information and products in the virtual environment has made it time consuming to acquire relevant information and knowledge amidst a vast amount of information. Therefore, an intelligent system that can offer the most appropriate and desirable among the large amount of information and products by following the conditions and features selected by each user should be essentially efficient. Systems that perform this task are called recommendation systems. Given the volume of social network data, challenges such as short-term processing and increased accuracy of recommendations are discussed in this type of system. Hence, it can perform processes faster with less error and can be effective in improving the performance of social recommending systems in improving the classification and clustering of information with the help of collaboration filtering methods. This study first develops an innovative conceptual model of a social network-based tourism recommendation system using Flicker network data. This model is based on 9 key components. The comparison show that the proposed method has an accuracy of 0.3% and a lower error rate

Growth Performance and Body Composition of Pikeperch (Sander lucioperca) Fingerlings under Dietary L-Carnitine

Abstract Growth performance, food efficiency and body composition of pikeperch (Sander lucioperca) were investigated with 1 or 2 g/kg L-carnitine added to the diet. Control diet did not contain L-carnitine. Two hundred pikeperch fingerlings (1.63 g, mean weight) were stocked in each 1 m3 concrete tank and fed equally 6 meals per day for 6 weeks. Higher increment in body weight (5.92 ± 0.37 g), the highest specific growth rate (3.75 ± 0.17) and food efficiency (98.04 ± 4.56), the highest crude protein (64.53 ± 0.84), the lowest crude lipid (21.59 ± 0.23) and significant (P<0.05) lowering in food conversion ratio (1.02 ± 0.05) were obtained with 2 g/kg L-carnitine diet. The greatest survival rate (84.88 ± 0.92) occurred when fingerlings were fed with 1 g/kg L-carnitine. Both treatments with L-carnitine showed lesser rate of cannibalism than in control (1.25± 0.09). Use of dietary L-carnitine improved growth performance and body composition of pikeperch fingerlings

Energy harvesting frictionless brakes for short-haul aircraft: thermal and electromagnetic feasibility of an axial-flux machine for a landing gear drive system

The aviation industry is currently responding to climate change with, among other technologies, electrification of aircraft, and the corresponding onboard electrical architecture provides an opportunity for electromagnetic brakes. The present work introduces a multistage yokeless and segmented armature (YASA) electric machine that replaces friction brakes and harvests kinetic energy throughout a landing. The study establishes the optimal trade-off between weight and electromagnetic torque and translates it into the design requirements for the development of an electric machine. Electromagnetic modeling is conducted using a quasi-3D transient approach and static 3D validation. The results reach 120 Nm/kg active material torque density at approximately 50 A/mm² current density. The proposed solution enables fitting an electric machine that decelerates an aircraft at autobrake level LOW for Airbus and the "1" and "2" settings for Boeing. A thermal analysis follows, where a novel cruise altitude cooling method is proposed.European Union funding: 251798

An optimized energy saving mechanism in IEEE 802.16e mobile WiMAX systems

The IEEE 802.16e standard defines a sleep mode operation for conserving power to support the battery life of mobile broadband wireless access (BWA) devices. The system saves energy when it goes through a sleep period with some delay in packet arrival response time. The relationship between energy consumption and the delay is studied to ensure best performance for mobile devices. This relationship has been analyzed by using a mathematical model. A new scheduling method is proposed to adjust the sleep cycle periods by adding a small increase to the next sleep cycle compared with the previous cycle instead of just simply doubling the previous cycle. The simulated results were obtained after adjusting the length of the first sleep cycle period (Tmin). Adjusting Tmin  provides a result of 54% reduction in the time needed for every frame to get a response especially in a lower traffic region. In a high traffic region, a reduction of 21.5% has been obtained in energy consumption for each sleep mode operation. Therefore, the proposed idea confirms a faster frame response time at lower energy consumption

Experimental investigation on the effect of wear flat inclination on the cutting response of a blunt tool in rock cutting

A vast majority of experimental researches focuses on the cutting action of a sharp cutter, while there has been limited experimental work devoted to the study of the contact process at the wear flat-rock interface. The specific objective of this study is to determine the effect of the wear flat inclination angle ( β ) with respect to the cutter velocity vector ( vv ) on both the contact stress ( σ ) and friction coefficient ( μ ) mobilized at the wear flat-rock interface. An extensive and comprehensive set of cutting experiments was carried out on thirteen different sedimentary quarry rock samples using a state-of-the-art rock cutting equipment. A unique cutter holder was purposely designed and manufactured along with a precise experimental protocol implemented in order to change the back rake angle and therefore the inclination β by steps of 0.10∘ . The experimental observations confirm the existence of three regimes of frictional contact (identified as elastic, elasto-plastic and plastic) for all rock samples. Further, the results suggest that the scaled contact stress is predominantly controlled by a dimensionless number η=E∗tanβq with E∗ the plane strain elastic modulus and q the rock strength

Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-Adjusted life-years for 29 cancer groups, 1990 to 2017 : A systematic analysis for the global burden of disease study

Importance: Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective: To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review: We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-Adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings: In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572000 deaths and 15.2 million DALYs), and stomach cancer (542000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601000 deaths and 17.4 million DALYs), TBL cancer (596000 deaths and 12.6 million DALYs), and colorectal cancer (414000 deaths and 8.3 million DALYs). Conclusions and Relevance: The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care. © 2019 American Medical Association. All rights reserved.Peer reviewe

Impact of Inaccurate User and Base Station Positioning on Autonomous Coverage Estimation

Autonomous monitoring of key performance indicators, which are obtained from measurement reports, is well established as a necessity for enabling self-organising networks. However, this reports are usually tagged with geographical location information which are obtained from positioning techniques and are therefore prone to errors. In this paper, we investigate the impact position estimation errors on the cell coverage probability that can be estimated from autonomous coverage estimation (ACE). We derive novel and accurate expressions of the actual cell coverage probability of such scheme while considering: errors in user equipment (UE) location and; errors in both UE and base station location. We present generic expressions for channel modelled with path-loss and shadowing, and much simplified expressions for the path-loss dominant channel model. Our results reveal that the ACE scheme will be suboptimal as long as there are errors in the reported geographical location information. Hence, appropriate coverage margins must be considered when utilising ACE