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

A scalable, non-interfering, synthesizable Network-on-Chip monitor – Extended version

This paper presents a hardware-accelerated Kvazaar HEVC intra encoder for 4K real-time video coding at up to 120 fps. The encoder is implemented on a Nokia AirFrame Cloud Server featuring a 2.4 GHz dual 14-core Intel Xeon processor and two Arria 10 PCI Express FPGA accelerator cards. The presented encoder is a speed-optimized version of our 1st generation 4K40p HEVC intra encoder. The proposed speedup techniques include 1) Increasing the number of FPGA cards to two; 2) Remapping the simplest multiplications from DSP blocks to logic for better FPGA utilization; 3) Making task scheduling more flexible to improve utilization rate of hardware accelerators; and 4) Increasing the pipeline depth and duplicating time-sensitive resources in the hardware accelerator. As a result, up to three hardware accelerator instances can be accommodated in a single Arria 10 so the encoder is able to make use of six accelerators. According to our experiments, the proposed encoder obtains threefold speedup over our 1st generation encoder. Our proposal is also shown to outperform all other encountered FPGA and ASIC implementations.

FPGA-Powered 4K120p HEVC Intra Encoder

Experimental parallel implementation of a wavelet-based still image encoder

Nowadays HTTP servers and applications are some of the most popular targets for network attacks. In this research, we consider an algorithm for HTTP intrusions detection based on simple clustering algorithms and advanced processing of HTTP requests which allows the analysis of all queries at once and does not separate them by resource. The method proposed allows detection of HTTP intrusions in case of continuously updated web-applications and does not require a set of HTTP requests free of attacks to build the normal user behaviour model. The algorithm is tested using logs acquired from a large real-life web service and, as a result, all attacks from these logs are detected, while the number of false alarms remains zero.

Detection of Anomalous HTTP Requests Based on Advanced N-gram Model and Clustering Techniques

One of the premier mechanisms used in extracting unobserved signals from observed mixtures in signal processing is employing a blind source separation (BSS) algorithm. Orthogonal frequency division multiplexing (OFDM) techniques are playing a prominent role in the sphere of multicarrier communication. A set of remedial solutions taken to mitigate deteriorative effects caused within the air interface of OFDM transmission with aid of BSS schemes is presented. Four energy functions are used in deriving the filter coefficients. Energy criterion functions to be optimized and the performance is justified. These functions together with iterative fixed point rule for receive signal are used in determining the filter coefficients. Time correlation properties of the channel are taken advantage for BSS. It is tried to remove colored noise and jamming components from the mixture at the receiver. The method is tested in a slow fading channel with a receiver containing equal gain combining to treat the channel state information values. The importance is that, these are quite low computational complexity mechanisms.

Timo Hämäläinen

Papers

A scalable, non-interfering, synthesizable Network-on-Chip monitor – Extended version

FPGA-Powered 4K120p HEVC Intra Encoder

Experimental parallel implementation of a wavelet-based still image encoder

Detection of Anomalous HTTP Requests Based on Advanced N-gram Model and Clustering Techniques

Blind source separation for OFDM with filtering colored noise and jamming signal