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

The specific objective of this study was to better understand Nigerian university students’ perceptions and readiness towards mobile learning. Recently, the influence of mobile technology is seen to have infiltrated everyday life and the learning institutions. It is thus crucial for learning institutions to assess and understand the factors advancing the mobile learning adoption. This study offers some important insights into mobile learning adoption especially in developing countries like Nigeria. Data for this study were collected using a survey. Undergraduate students at two Nigerian universities (N = 135) were non-randomly allocated to respond to a survey. Overall, the results reveal the existence of the widespread use and ownership of a mobile phone by the Nigerian students. Also deduced, is that a good number of the courses require the use of internet for completion of course assignments. Therefore, we recommend that course resources should be designed for delivery through a mobile device.

Exploring Nigerian University Students’ Perception towards Mobile Learning

Wireless Sensor Networks (WSNs) consist of embedded and distributed sensor nodes that operate on harsh operating conditions and with limited energy resources. To ensure the desired level of service, it is essential to detect and correct occurring network and node problems. In this paper, we propose a diagnostics software architecture for WSNs consisting of self-diagnostics on embedded sensor nodes and management tools for network analysis. We define a minimum set of diagnostics information that needs to be collected for analyzing the network errors and performance. To minimize communication overhead, collected information is categorized and only needed categories are requested from nodes. The diagnostics architecture is verified with a practical WSN implementation.

Embedded Software Architecture for Diagnosing Network and Node Failures in Wireless Sensor Networks

On the realization of periodic functions

In modern multimedia applications, memory bottleneck can be alleviated with special stride data accesses. Data elements in stride access can be retrieved in parallel with parallel memories, in which the idea is to increase memory bandwidth with several memory modules working in parallel and feed the processor with only necessary data. Arbitrary stride access capability with interleaved memories is described in previous research where the skewing scheme is changed at run time according to the currently used stride. This paper presents the improved schemes which are adapted to parallel memories. The proposed novel parallel memory implementation allows conflict free accesses with all the constant strides which has not been possible in prior application specific parallel memories. Moreover, the possible access locations are unrestricted and the accessed data element count equals to the number of memory modules. Timing and area estimates are given for Altera Stratix FPGA and 0.18 micrometer CMOS process with memory module count from 2 to 32. The FPGA results show 129 MHz clock frequency for a system with 16 memory modules when read and write latencies are 3 and 2 clock cycles, respectively. The complexity of the proposed system is shown to be a trade-off between application specific and highly configurable parallel memory system.

Timo Hämäläinen

Papers

Exploring Nigerian University Students’ Perception towards Mobile Learning

Embedded Software Architecture for Diagnosing Network and Node Failures in Wireless Sensor Networks

On the realization of periodic functions

Configurable data memory for multimedia processing

Field programmable gate array-based PCI interface for a coprocessor system