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

In this paper we present the Flow-based Fast Handover for Mobile IPv6 (FFHMIPv6) to speed up the handover process of the standardized Mobile IPv6 (MIPv6) protocol. The location registration processes related to the MlPv6 method cause delays dependant of the distance of the corresponding nodes, thus the delays might be considerable. The FFHMIPv6 uses the flow state information and IPv6-in-IPv6 encapsulation to enable the reception of flows simultaneously with the location update processes. We analyze the method mainly with Network Simulator 2 simulations, but also a glimpse to Mobile IPv6 for Linux experiments is presented. The FFHMIPv6 is found to be an efficient and simple way to speed up the downstream handover delay. Also, some ideas about the upstream traffic are presented.

A Fast Handover Method for Mobile IPv6 Networks

IEEE 802.16j is a wireless broadband networking standard that integrates infrastructure base stations with multihop relay technology. Relay stations can provide substantial gains of coverage area and capacity of the cell compared to a base station alone. In this paper, we introduce an optimization problem of cost-effective relay station deployment for the IEEE 802.16j networks in the case of extending the coverage on the new residential area. First, the coverage area, which is defined as an area where the user has a signal- to-noise value greater than a given threshold value, is calculated. After that, the number of relay stations and their optimal positions which maximize the provider's profit are found, taking into account one-time costs, such as CAPEX and IMPEX, as well as recurring costs, like OPEX. The proposed relay deployment mechanism allows the providers to maximize their own profit if they want to extend the coverage area to attract new customers.

A Relay Deployment Mechanism for One Scenario of Cost-Effective Coverage Extension in IEEE 802.16j Networks

Wireless sensor network (WSN) is a technology comprising even thousands of autonomic and self-organizing nodes that combine environmental sensing, data processing, and wireless multihop ad-hoc networking. The features of WSNs enable monitoring, object tracking, and control functionality. The potential applications include environmental and condition monitoring, home automation, security and alarm systems, industrial monitoring and control, military reconnaissance and targeting, and interactive games. This chapter describes low-power WSN as a platform for signal processing by presenting the WSN services that can be used as building blocks for the applications. It explains the implications of resource constraints and expected performance in terms of throughput, reliability and latency.

Low-Power Wireless Sensor Network Platforms

In this study, we apply an anomaly-based approach to analyze traffic flows transferred over a network to detect the flows related to different types of attacks. Based on the information extracted from network flows a model of normal user behavior is discovered with the help of several clustering techniques. This model is then used to detect anomalies within recent time intervals. Since this approach is based on normal user behavior, it can potentially detect zero-day intrusions. Moreover, such a flow-based intrusion detection approach can be used in high speeds since it is based on information in packet headers, and, therefore, has to handle a considerably lesser amount of data. The proposed framework is tested on the data obtained with the help of a realistic cyber environment (RGCE) that enables one to construct real attack vectors. The simulations show that the proposed method results in a higher accuracy rate when compared to other intrusion detection techniques.

Online detection of anomalous network flows with soft clustering

This paper presents our experiences in teaching System-on-Chip design at Tampere University of Technology. We concentrate on teaching practices and the exercise work which teaches intellectual property (IP) reuse, platform-based design, and HW/SW co-design. Students implement a video encoder on FPGA. It has proved good that teachers split the large exercise into mandatory weekly tasks and bonus points are awarded for good work. Automated testbenches and startup examples were very useful. Students consider the course motivating, even if they underestimate the required time and effort. In the future, more effort is needed in finalizing the reusable IP blocks and whole project works for easier reuse as well as setting up a common repository.

https://ieeexplore.ieee.org/iel7/6868743/6877377/06877391.pdf

Timo Hämäläinen

Papers

A Fast Handover Method for Mobile IPv6 Networks

A Relay Deployment Mechanism for One Scenario of Cost-Effective Coverage Extension in IEEE 802.16j Networks

Low-Power Wireless Sensor Network Platforms

Online detection of anomalous network flows with soft clustering

Experiences from System-on-Chip design courses