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

SUMMARY This paper proposes a scheduling model for ensuring delay as a Quality of Service requirement in the communications network. Different delays are allocated for different pricing classes, say gold, silver, and bronze classes. Our purpose is to minimize weighted mean delay for connections, where weights are the pricing factors of different classes. An adaptive and optimal solution is derived for weights of the scheduler. Simulations show that in addition to the mean delay minimization, the revenue of the service provider is also maximized in the linear pricing scenario. In addition, adaptive updating rule is simple to implement, since it converges fast. Our scenario is independent on the statistical assumptions, and therefore it is robust against possible erroneous estimates of the customers' behavior.

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Delay minimization and pricing method for the network services

Parallel realizations of Kanerva's sparse distributed memory on a tree-shaped computer

This paper presents an information-theoretic approach to variable selection for prediction of laboratory measurements of paper quality. Along with a well-known Principal Component Analysis we considered techniques for variable selection based on the classical Shannon Mutual Information and a novel Maximal Information Coefficient. A multilayer perceptron neural model was used to predict quality measurements and compare feature selection techniques. The suggested approach was tested on real industrial data obtained form a pilot paper machine. The presented results show that information-theoretic techniques perform better compared to Principal Component Analysis, providing higher accuracy results.

Information-theoretic approach to variable selection in predictive models applied to paper machine data

During the recent years, high-level synthesis (HLS) has gained traction as a viable alternative to traditional handwritten register transfer level code in describing digital systems. This has been attributed to the maturing of the HLS tools and improving quality of their results. However, most published applications are data path intensive as HLS offers good tools for loop optimization, such as pipelining and loop unrolling. HLS is seldom applied to control-oriented applications since clock is not explicitly present in HLS source code. In this paper, we show how a clock cycle accurate application can be described with HLS. We give as a proof of concept an implementation of an FPGA-based I2C bus controller for an audio codec using Catapult C, and present a generalized work flow. Compared with a corresponding handwritten VHDL implementation, the HLS version consumes 84% more area at the same performance but productivity is increased by 100% at the first design time and even more with further design iterations.

Designing a clock cycle accurate application with high-level synthesis

This paper presents an adaptive resource allocation model that is based on the DRR queuing policy. The model ensures QoS requirements and tries to maximize a service provider's revenue by manipulating quantum values of the DRR scheduler. To calculate quantum values, it is proposed to use the revenue criterion that controls the allocation of free resources. The simulation considers a single node with the implemented model that serves several service classes with different QoS requirements and traffic characteristics. It is shown that the total revenue can be increased due to the allocation of unused resources to more expensive service classes. At the same time, bandwidth and delay guarantees are provided. Furthermore, the adaptive model eliminates the need to find the optimal static quantum values because they are calculated dynamically.

Timo Hämäläinen

Papers

Delay minimization and pricing method for the network services

Parallel realizations of Kanerva's sparse distributed memory on a tree-shaped computer

Information-theoretic approach to variable selection in predictive models applied to paper machine data

Designing a clock cycle accurate application with high-level synthesis

Revenue-Based adaptive deficit round robin