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

Network traffic is increasing all the time and network services are becoming more complex and vulnerable. To protect these networks, intrusion detection systems are used. Signature-based intrusion detection cannot find previously unknown attacks, which is why anomaly detection is needed. However, many new systems are slow and complicated. We propose a log anomaly detection framework which aims to facilitate quick anomaly detection and also provide visualizations of the network traffic structure. The system preprocesses network logs into a numerical data matrix, reduces the dimensionality of this matrix using random projection and uses Mahalanobis distance to find outliers and calculate an anomaly score for each data point. Log lines that are too different are flagged as anomalies. The system is tested with real-world network data, and actual intrusion attempts are found. In addition, visualizations are created to represent the structure of the network data. We also perform computational time evaluation to ensure the performance is feasible. The system is fast, finds intrusion attempts and does not need clean training data.

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An Efficient Network Log Anomaly Detection System Using Random Projection Dimensionality Reduction

This paper presents the design, implementation, and practical real world experiments of an energy optimized multi-hop wireless sensor network (WSN) targeted at environmental monitoring. The WSN is fully autonomous and consists of energy-efficient and scalable communication protocols and low-power hardware platform. Software tools are developed for configuring and analyzing large scale networks. The network has been deployed in outdoor environment consisting of 20 nodes covering over 2 km2 area. The results show that the multi-hop network works autonomously, reacts to environmental changes, and is able to operate temperatures down to -30 °C. The hardware nodes operating on 433 MHz frequency provide over 1 km communication distances, while still having sufficient throughput and low energy consumption. The deployed nodes had a lifetime of 6 months with a 1600 mAh battery, while generating 4 packets per minute.

Design, implementation, and experiments on outdoor deployment of wireless sensor network for environmental monitoring

The IEEE 802.16 standard defines a wireless broadband technology called WiMAX. When compared with other wireless technologies, it introduces many interesting advantages at PHY, MAC, and QoS layers. Heavy simulations are needed to study the performance of IEEE 802.16 and propose further enhancements to this standard. Link-level simulations are not always sufficient, while system-level simulators are not always accurate enough to capture MAC and transport protocol details. We implemented an 802.16 extension for the NS-2 network simulator. It includes upper PHY modeling, almost all of the features of the 802.16 MAC layer, as well as the QoS framework. This article describes the implemented features and simulation methodology, while sharing our experience that can be used with other NS-2 modules. There is also an overview of the past and ongoing research.

WINSE: WiMAX NS-2 extension

The IEEE 802.16 technology defines the ARQ mechanism that enables a connection to resend data at the MAC level if an error is detected. In this paper, we analyze the ARQ aware scheduling for the 802.16 base station. In particular, we consider how the BS scheduler can account for the ARQ block size, absence of the ARQ block rearrangement, and the ARQ transmission window. We propose a set of constraints that can be applied to any base station scheduler algorithm. To test them, we run a number of simulation scenarios. The simulations results confirm that the ARQ aware scheduling can improve the overall performance.

ARQ Aware Scheduling for the IEEE 802.16 Base Station

Two optimized implementations of the emerging ITU-T H.26L video encoder are described. The first, medium-optimized version, is implemented in C and the latter, highly optimized version, utilizes both algorithmic and platform-specific optimizations. Comparisons to a correspondingly optimized H.263/H.263+ implementation are given with the spatial and temporal video quality fixed and the bit rate and complexity varied. On a 733 MHz general-purpose processor, an average encoding speed of 17 frames per second for QCIF sequences is achieved with a 29% reduction in bit rate compared to H.263+. The complexity of H.26L is about 3.4 times more than that of H.263+.

Timo Hämäläinen

Papers

An Efficient Network Log Anomaly Detection System Using Random Projection Dimensionality Reduction

Design, implementation, and experiments on outdoor deployment of wireless sensor network for environmental monitoring

WINSE: WiMAX NS-2 extension

ARQ Aware Scheduling for the IEEE 802.16 Base Station

Performance of H.26L Video Encoder on General-Purpose Processor