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

Modelling Medical Devices with Honeypots: A Conceptual Framework

This paper presents the usage of the specification and description language (SDL) for system simulations. The two studied systems are a large-scale telecommunication system for delivering information services to public transport passengers (TUTPIS), and an embedded medium access control (TUTMAC) protocol for wireless LAN. The TUTPIS system combines the implementation of some systems components by the building of a simulation model for the general service architecture. On the other hand, TUTMAC has been fully implemented in SDL. The formal system designs are simulated for verifying the required functionality. By simulations, the architectural operability of the transport service system design has been tested, e.g. with a very high number of users and with different telecommunication network environments. For the embedded TUTMAC protocol, real-time simulations are performed for evaluating the performance and capacity requirements of the application for the final platform.

Using SDL a tool for system simulations

Sensor node platforms implement the physical layer (hardware) of the protocol stack. The hardware activity measured as the fraction of time the hardware is in an active state (processing data or receiving/transmitting a packet) may be below 1% in low data-rate monitoring applications. Thus, it is very important to minimize the power consumption in idle and sleep modes.

Hardware Platforms and Components

Code Division Multiple Access (CDMA) techniques are a group of governing technologies used in multiple access communication. One of the premier mechanisms used in extracting unobserved signals from observed mixtures in signal processing is employing a Blind Source Separation (BSS) algorithm. Two solutions of a series taken to mitigate deteriorative effects caused within the air interface of a basic CDMA system with aid of BSS schemes is presented in this paper. In the short code model, time correlation properties of the channel is taken advantage for BSS and it is tried to remove the noise. A scalar energy function of receive signal is used with the iterative fixed point rule in determining the filter coefficients at the receiver. A system model with a slow fading downlink channel is used for testing purposes. Path gain values are treated with Equal Gain Combining (EGC). They can be pronounced as low computational complexity mechanisms.

http://ieeexplore.ieee.org/iel5/6302083/6316561/06316658.pdf

Blind Source Separation for DS-CDMA with filtering colored noise

This paper presents a class of service (CoS) protocol to be used on wireless LANs (WLAN). The protocol provides two modes for bandwidth control and uses centrally controlled topology. The first mode reserves a fixed bandwidth for a station, while in the second mode the station must request a permission to send data. A traffic scheduler for the network is located in the central controller station. A prototype of the protocol operating on IEEE802.11 WLAN has been implemented for testing. Results show that the protocol provides CoS support that differentiates bandwidth and transfer delay for each class.

Timo Hämäläinen

Papers

Modelling Medical Devices with Honeypots: A Conceptual Framework

Using SDL a tool for system simulations

Hardware Platforms and Components

Blind Source Separation for DS-CDMA with filtering colored noise

Class of service control protocol for wireless LAN