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

This article presents an efficient method to capture abstract performance model of streaming data real-time embedded systems (RTESs). Unified Modeling Language version 2 (UML2) is used for the performance modeling and as a front-end for a tool framework that enables simulation-based performance evaluation and design-space exploration. The adopted application meta-model in UML resembles the Kahn Process Network (KPN) model and it is targeted at simulation-based performance evaluation. The application workload modeling is done using UML2 activity diagrams, and platform is described with structural UML2 diagrams and model elements. These concepts are defined using a subset of the profile for Modeling and Analysis of Realtime and Embedded (MARTE) systems from OMG and custom stereotype extensions. The goal of the performance modeling and simulation is to achieve early estimates on task response times, processing element, memory, and on-chip network utilizations, among other information that is used for design-space exploration. As a case study, a video codec application on multiple processors is modeled, evaluated, and explored. In comparison to related work, this is the first proposal that defines transformation between UML activity diagrams and streaming data application workload meta models and successfully adopts it for RTES performance evaluation.

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Performance evaluation of UML2-modeled embedded streaming applications with system-level simulation

This paper analyzes parallel scalability and coding speed of our open-source Kvazaar HEVC intra encoder on Intel Xeon Phi 61-core coprocessor that supports up to four hardware threads per core. The evaluated parallelization schemes of Kvazaar are 1) Wavefront Parallel Processing (WPP); and 2) tiles, both accelerated with picture-level parallel processing. With WPP, the C implementation of Kvazaar high-quality preset achieves an average speedup of 1.3 and a bit rate gain of 0.7% over the respective implementation of x265. Using tiles makes Kvazaar 1.4 times faster than x265 but at a cost of 0.3% bit rate loss. When high-speed presets are used, the speedup of Kvazaar increases to 1.4 with WPP and to 1.9 with tiles. Moreover, the respective coding efficiency of Kvazaar rises to 11.2% and 10.3%. Kvazaar also scales almost linearly to the number of cores in the processor. Even if the peak coding speed of Kvazaar on Xeon Phi is lower than that on the Intel 8-core i7 processor, our parallel scalability results promise excellent speed for Kvazaar on massively parallel processors equipped with more powerful cores.

Timo Hämäläinen

Papers

Performance evaluation of UML2-modeled embedded streaming applications with system-level simulation

Performance evaluation of Kvazaar HEVC intra encoder on Xeon Phi many-core processor

Interconnection scheme for continuous-media systems-on-a-chip

Flow-based fast handover for mobile IPv6 environment - implementation and analysis

Address Computation in Configurable Parallel Memory Architecture