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

Wireless sensor networks function as one of the enablers for the large-scale deployment of Internet of Things in various applications, including critical infrastructure. However, the open communications environment of wireless systems, immature technologies and the inherent limitations of sensor nodes make wireless sensor networks an attractive target to malicious activities. The main contributions of this review include describing the true nature of wireless sensor networks through their characteristics and security threats as well as reflecting them to network anomaly detection by surveying recent studies in the field. The potential and feasibility of graph-based deep learning for detecting anomalies in these networks are also explored. Finally, some remarks on modelling anomaly detection methods, using appropriate datasets for validation purposes and interpreting complex machine learning models are given.

Network Anomaly Detection in Wireless Sensor Networks: A Review

This chapter focuses on parallel implementations of the Self-Organizing Map (SOM) featuring different levels of parallelism. The basic arithmetic-logical operations of SOM are first reviewed for a consideration of implementation issues such as number precision, memory consumption and time complexity. Mapping involves network, training set, neuron and weight parallelism. Examples of the weight and neuron parallel mappings are given for abstract platforms to conduct general principles. Neuron parallel mapping is considered in great detail as it is the most commonly used approach. A review of implementations is given from supercomputers to VLSI (Very Large Scale Integration) chips with criteria for performance comparison.

Parallel implementation of self-organizing maps

A parallel implementation of H.263/MPEG-4 video encoder for Common Intermediate Format (CIF, 352/spl times/288) pictures is presented. The implementation runs on Hunt Engineering's Hepc/spl delta/ DSP-carrier featuring four TMS320C6201 ICs. The experimental results show real-time encoding speed of 30 fps has been reached using configuration of a master and two slave encoding DSPs. In addition, DSP-to-DSP link requirements, image quality vs. bit rate, scalability and frame rate performance are measured and analyzed.

Parallel implementation of H.263 encoder for CIF-sized images on quad DSP system

This paper presents automated distribution of embedded real-time applications modeled in Unified Modeling Language version 2.0 (UML 2.0). The automated distribution requires methods and tools for design automation, as well as the run-time environment for the distributed execution on the target platform. Executable application code is generated from UML models, and UML with a custom profile is used to abstract hardware architecture and configure application mapping. For experimenting, a full featured WLAN terminal was designed in UML and implemented as a distributed multiprocessor system-on-chip (SoC) on an FPGA prototype platform. Measurements show that a 50-70% reduction in protocol delays is achived with distribution, and delay variations are reduced 45-85%.

Automated distribution of UML 2.0 designed applications to a configurable multiprocessor platform

Comparison of six data dependence analysis algorithms is presented. The algorithms are purposed for a parallel compiler that is being developed for a configurable multi-DSP system PARNEU. The algorithms are implemented in SUIF compiler framework and benchmarked with Perfect Club, Audio Signal Processing, and Media Bench test problems. Proprietary PARNEU programs that have been manually parallelised are also included. Performance in terms of accuracy and execution time of the data dependence algorithms has been measured and compared. The results show that the Omega test is the most accurate but also takes most execution time for benchmarks with for-loop parallelism.

Timo Hämäläinen

Papers

Network Anomaly Detection in Wireless Sensor Networks: A Review

Parallel implementation of self-organizing maps

Parallel implementation of H.263 encoder for CIF-sized images on quad DSP system

Automated distribution of UML 2.0 designed applications to a configurable multiprocessor platform

Comparison of Data Dependence Analysis Tests