There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

The PASCAL Visual Object Classes Challenge

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

The development of software for modern products with lots of interfaces, layers and stakeholders has become very complex, increasing the risk of inefficiency. Key Performance Indicators (KPIs) can be used to identify bottlenecks and problems, but the challenge is how to create KPI models, processes and dashboards that help improving the development processes and can be adopted by all the stakeholders. We introduce the RelAA Framework - a bottom-up approach for monitoring product-focused software development. The RelAA (Relevant, Accessible and Adoptable) Framework is created in an industrial setup that currently includes around 350 persons in different phases of the software life cycle. The RelAA Framework is formed by analyzing existing KPIs and tools, gathering feedback from development teams, management, business representatives, and other stakeholders, and creating intuitive ways to share information related to KPIs. The RelAA Framework itself does not define exact KPIs for the organization to adopt, but it provides a process and model how to create, document and utilize KPIs. The RelAA Framework ensures relevance, accessibility, and adoption of KPIs across stakeholders and organization.

Framework for Creating Relevant, Accessible, and Adoptable KPI Models in an Industrial Setting.

In Low Earth Orbit (LEO) satellites, which run in polar orbit, the area of overlap among beams becomes wider as the latitude of satellites increases, which leads to intolerable interference and extra energy consumption. To minimize the onboard power with QoS requirements, we propose an energy optimization model with considering power allocation, user association and dynamic beam ON/OFF operation jointly. Moreover, the frequent beam ON/OFF operations lead to the large number of user handovers, so handover cost is also considered in the model. The original problem is decomposed into two levels due to the high coupling of variables and the successive convex approximation is employed. A low complexity greedy ON/OFF iteration is proposed to adapt to dynamic topology of LEO. Simulation results show that the proposed scheme can effectively reduce the system energy consumption.

Joint User Association and Dynamic Beam Operation for High Latitude Muti-beam LEO Satellites

This paper considers signaling protocols for the DiffServ QoS framework. Originally, DiffServ had no standardized signaling solution, which resulted in the static configuration for the DiffServ domain. However, the dynamic allocation of resources within the domain allows to ensure the per-flow QoS guarantees and achieve better performance. At the moment, several signaling solutions for DiffServ framework are available. Thus, it is crucial to analyse these solutions and interconnections between them. In particular, the RSVP, aggregated RSVP, GIST, COPS, and SIP protocols will be considered. The simulation comprises several scenarios that present that the dynamic allocation of resource within the domain can provide considerably better results when compared to the static configuration.

Analysis and simulation of the signaling protocols for the diffserv framework

In this paper, we propose a short-packet secure UAV-aided data collection and transmission scheme to guarantee the freshness and security of the transmission from the sensors to the base station (BS). First, for the data collection phase, the trajectory, the flight duration, and the user scheduling are jointly optimized with the objective to maximize the energy efficiency (EE). To solve the non-convex EE maximization problem, we adopt the first-order Taylor expansion to convert it into two convex subproblems, which are then solved via successive convex approximation. Furthermore, we consider the maximum rate transmission in the UAV data transmission phase to achieve a maximum secrecy rate. The transmit power and the blocklength of UAV-to-BS transmission are jointly optimized subject to the constraints of eavesdropping rate and outage probability. Simulation results are provided to validate the effectiveness of the proposed scheme.

Energy-Efficient Secure Data Collection and Transmission via UAV

In this paper we used wind power and meteorological data provided by a Finnish energy company and the Finnish Meteorological Institute as the research material. The study determined the most important factors which had influence to the effectiveness of the wind turbine power production. This was done by using the physical power function with statistical data analysis. Wind speed was found to be the most significant factor for the model. This was due to the fact that wind speed was the only variable which affect was exponential. Another significant factor when it comes to creating a forecast model was temperature. The affect wasn't as powerful as with wind speed but still notable. These observations were also confirmed in statistical interpretation. A tailored forecasting model was formed for our target wind turbine on the basis of these factors: suitable modelling for necessary meteorological factors was executed and the coefficient factor was calculated. The results and especially the forecast model was seen significant and would be used in creation of a production forecast program's first version for the energy company in question.

Timo Hämäläinen

Papers

Framework for Creating Relevant, Accessible, and Adoptable KPI Models in an Industrial Setting.

Joint User Association and Dynamic Beam Operation for High Latitude Muti-beam LEO Satellites

Analysis and simulation of the signaling protocols for the diffserv framework

Energy-Efficient Secure Data Collection and Transmission via UAV

Wind Turbine Sensor Data Analysis and Production Forecast