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

Chips have become the critical asset of the technology, and increasing effort is put to design System-on-Chips (SoC) faster and more affordable. Typically the focus of the research has been on the Power, Performance and Area optimization of the specific component or sub-system. To improve the situation we report design effort for complex SoC counted from specification to ASIC tape-out to lay out a solid reference for the community. Ballast is the first SoC-Hub chip taped out on 22nm technology. It includes six sub-systems on 15 mm2area and reaches 1.2GHz top speed. The design team included 24 persons and spent 21 200 person hours to tape-out in one calendar year from scratch. This is an outstanding achievement and sets the baseline to SoC design productivity development.

Ballast: Implementation of a Large MP-SoC on 22nm ASIC Technology

Mitigation of inter-carrier (ICI) and inter-symbol interference (ISI) in the systems based on Orthogonal Frequency Division Multiplexing (OFDM) has been a popular research topic already for several decades. Utilisation of signal bases with ameliorated time-frequency (TF) localisation is one of the methods to increase interference robustness. However, this approach results in more complicated signal processing. In this paper we theoretically derive and formulate computationally efficient algorithms for modulation of discrete finite-dimensional signals constructed from well-localised bases. These results can be used directly for software implementation. In addition, we present the comparison of computing load for straightforward matrix multiplication, for developed algorithm and for Fast Fourier Transform (FFT) used in OFDM. This study shows that the number of operations can be significantly reduced allowing signal processing with a great number of sub-carriers and time shifts in a reasonable time.

Computationally efficient modulation of well-localised signals for OFDM

Delay Jitter simulations and measurements of time-critical multimedia components

Embedded Computer Systems: Architectures, Modeling, and Simulation: 6th International Workshop, SAMOS 2006, Samos, Greece, July 17-20, 2006, Proceedings (Lecture Notes in Computer Science)

Design space exploration is used to shorten the design time of System-on-Chips (SoCs). The models used in the exploration need to be both accurate and fast to simulate. This paper introduces a multi-level communication cost to improve the accuracy of the abstracted system models. During the simulation, one of three different communication costs is applied for each inter-task communication event based on the mapping of the communicating tasks. The accuracy of three system abstraction models including the presented communication cost is evaluated using a Motion-JPEG (M-JPEG) application described in Unified Modeling Language (UML). According to the results, the average error in frames per second (FPS) is 3.8% for the trace model, 4.3% for the modulo model, and 12.8% for the probabilistic model compared to FPGA execution. The results show that with the multi-level communication cost the accuracy is increased significantly, and accurate results can be achieved with arbitrary mappings.

Timo Hämäläinen

Papers

Ballast: Implementation of a Large MP-SoC on 22nm ASIC Technology

Computationally efficient modulation of well-localised signals for OFDM

Delay Jitter simulations and measurements of time-critical multimedia components

Embedded Computer Systems: Architectures, Modeling, and Simulation: 6th International Workshop, SAMOS 2006, Samos, Greece, July 17-20, 2006, Proceedings (Lecture Notes in Computer Science)

Evaluating the Model Accuracy in Automated Design Space Exploration