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

Research of web-servers has recently addressed the problem of content distribution coupled with quality of service (QoS). Due to the explosive growth of services offered over the Internet, novel mechanisms are needed for IP based service delivery to scale in a client-transparent way. This paper addresses the above problem considering also utilization of available processing power of servers. Many developed caching systems dedicate a fixed portion of the processing power for higher QoS services leading to lowered overall throughput of the server system. Here we introduce and simulate a QoS aware caching scheme that offers lower response delay for higher quality services and additionally optimizes utilization of the available processing power.

/pdf/performance-simulations-of-a-qos-aware-caching-method-29hzs9kqrq.pdf

Performance Simulations of a QoS Aware Caching Method

Security Enhancement Layer for Bluetooth

Data compression is a common requirement for displaying large amounts of information. The goal is to reduce visual clutter. The approach given in this paper uses an analysis of a data set to construct a visual representation. The visualization is compressed using the address ranges of the memory structure. This method produces a compressed version of the initial visualization, retaining the same information as the original. The presented method has been implemented as a Memory Designer tool for ASIC, FPGA and embedded systems using IP-XACT. The Memory Designer is a user-friendly tool for model based embedded system design, providing access and adjustment of the memory layout from a single view, complementing the "programmer's view" to the system.

Visualization of Memory Map Information in Embedded System Design

Wireless Sensor Networks (WSNs) consist of densely deployed, independent, and collaborating low cost sensor nodes. The nodes are highly resource-constrained in terms of energy, processing, and data storage capacity. Thus, the protocols used in WSNs must be highly energy-efficient. WSN communication protocols achieving the lowest power consumption minimize radio usage by accurately synchronizing transmissions and receptions with their neighbors. In this paper, we show how network signaling frames of state-of-the-art synchronized communication protocols for low-power WSNs supporting mobile nodes can be used for positioning. We derive mathematical models for node power consumption analysis. Both centralized and distributed positioning architectures are modeled. The models provide a tool for estimating what kind of network lifetimes can be expected when average positioned node speed, the amount of anchor nodes required by the location estimation algorithm, and the location refresh rate required by the application are known. The presented analysis results are based on two kinds of node hardware: with and without Received Signal Strength Indicator (RSSI). The results show that the positioning parameters and used hardware have significant impact on node power consumption and network lifetime. In the presented results, the network lifetime ranges from over 10 years to 2 months with different positioning requirements and hardware. keywords: Wireless, Low-Power, Sensor Networks c ⃝[2010] IEEE. Portions reprinted, with permission, from [Ville Kaseva, Timo D. Hamalainen, and Marko Hannikainen, Positioning in Resource-Constrained Ultra Low-Power Wireless Sensor Networks, Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), October/2010]

http://www.gnss.com.au/JoGPS/v10n1/JoGPS_v10n1p50-66.pdf

Positioning in Synchronized Ultra Low-Power Wireless Sensor Networks

DSP and particularly multimedia systems are among the most rapidly developing areas of digital IC (Integrated Circuit) design Multimedia hardware designers face the same time-to-market problems as the rest of the IC designers but, in addition, a large number of data transfers is required In this paper we utilize a novel IP (Intellectual Property) interface solution based on our Heterogeneous IP-Block Interconnection (HIBI) scheme The purpose of this scheme is to simplify design reuse in multimedia system development by acting as an efficient interconnection between heterogeneous IP-blocks As an example, a multimedia hardware environment based on the HIBI scheme is presented This paper shows how designing the environment out of reuseable blocks complying with the HIBI scheme is straightforward, and the result is still flexible and efficient The performance metrics of the environment are studied with the aid of a video encoder example With the encoder, real-time operation for QCIF (Quarter Common Intermediate Format) video can be achieved

Timo Hämäläinen

Papers

Performance Simulations of a QoS Aware Caching Method

Security Enhancement Layer for Bluetooth

Visualization of Memory Map Information in Embedded System Design

Positioning in Synchronized Ultra Low-Power Wireless Sensor Networks

Reuseable interface in multimedia hardware environment