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

Here we present the end-to-end QoS mechanism in 3G-multiaccess network environment. As multi-access wireless WLAN and wired xDSL wideband multi-access technologies has emerge and become more popular a need for interoperability with different technologies and domains has become necessity. There is also a need for end-to-end QoS management. We show a scenario where the UE-GGSN connection is covered by RSVP and RAN network part uses partial over dimensioning and real-time controlled ATM queuing. DiffServ covers WLAN-Core QoS and radio interface between WLAN AP and WLAN UE uses IEEE's 802.11e. Our interest is to find out how well 3G traffic classes can survive in different traffic conditions in the end-to-end case.

Providing QOS in 3G-WLAN environment with RSVP and DIFFSERV

This work presents a brief comparison of both academic and commercial intellectual property (IP) components. The aim is to gain insight to contemporary IP components regarding their application domains and cost in terms of area and energy. It has been estimated that in the future the maximum size of components must be in the range of 50-100 kilogates. According to this study, contemporary IP components mostly conform to this size limit. Selecting the most appropriate IP candidates is crucial for successful SoC design. However, IP comparison and selection is hard since even basic information is often missing.

Comparison of hardware IP components for system-on-chip

Sensor Node Platforms

The complexity of the physical and logical structures of the 3/4G network and service environment demand more sophisticated and complicated tools to be fully controllable and well managed. One of the main problems is the traditional telemanagement model that does not suit for managing fast and constantly changing network and system environment. The present way using nonreal time off-line tools to tune the tele system and networks in periods of some weeks or months when they would need constant real time control to overcome fast and unpredictable video streaming or call type traffic burst. To ease the difficult situation and solve this problem we have studied and developed a new control and management concepts and methods to monitor, control and manage in real time any magnitude of network and system environments. This paper presents a concept of the management system for large operator environments.

Monitoring and management solutions for 3G/4G wireless mobile networks

System-on-chip (SoC) designs with parallel on-chip memories increase the memory bandwidth and feed the processor with only necessary data. This paper presents the implementation of a novel Configurable Parallel Memory Architecture (CPMA) that offers plenty of design and run-time configuration possibilities. CPMA is shown to decrease considerably the processor-memory bottleneck by widening the memory bandwidth, decreasing the number of memory accesses, and diminishing the significance of memory latency.

Timo Hämäläinen

Papers

Providing QOS in 3G-WLAN environment with RSVP and DIFFSERV

Comparison of hardware IP components for system-on-chip

Sensor Node Platforms

Monitoring and management solutions for 3G/4G wireless mobile networks

Configurable Parallel Memory Implementation For System-on-Chip Designs