Timo Hämäläinen

Bio: Timo Hämäläinen is an academic researcher from University of Jyväskylä. The author has contributed to research in topics: Quality of service & Encoder. The author has an hindex of 38, co-authored 560 publications receiving 7648 citations. Previous affiliations of Timo Hämäläinen include Dalian Medical University & Nokia.

Implementation of encryption algorithms on transport triggered architectures

Abstract: The paper studies a configurable processor architecture, transport triggered architecture (TTA), for encryption algorithm implementations. The automatic TTA design space exploration is applied and configurations with good cost-performance ratio are found. It is shown that TTAs are at least equal to commercial processors in performance. According to earlier studies the performance level is also achieved at far lower cost. This encourages further development with tuned functionality.

Scalable implementation of H.263 video encoder on a parallel DSP system

Abstract: A parallel implementation of H.263 video encoder is presented for video conferencing applications. The parallel mapping has low communication and memory requirements and is scalable, which allows encoding of any of the five standard H.263 picture formats in real-rime. The presented parallelization method is implemented in a linearly expandable multiprocessor system called PARNEU, which includes very versatile communication topology. With the prototype system using four ADSP-21062 DSPs, a real-time encoding is achieved with QCIF sized picture. Performance estimations given for a larger system show very good speed-up figures.

Multicast access control concept for xDSL-customers

Abstract: Multicast is a tempting possibility for many broad- band services. It makes possible to deliver one data-stream to several receivers simultaneously. IP-Multicast is based on an open group concept. This means that it is possible for all the users to join the group and thus receive the data. The open concept is also the main reason why multicast has not been taken in wider use. There is two different solution to solve this problem, group access control and multicast data encryption. Group access control mechanisms focuses on restricting the group membership at the users edge device. Traffic encryption scheme relies on end-to-end encryption, so a key management architecture is also needed. We introduce our own proposal for a multicast group access control mechanism. Our mechanism consists of user authentication and dynamic access control list configuration. Our proposal is protocol independent and thus easy to take in use in various content delivery network environments.

Implementation of wireless LAN access point with quality of service support

Abstract: The maintaining of the quality of service (QoS) of the data transfers in wireless local area networks (WLAN) is an extensive task. An implementation of an access point (AP) providing an access from WLANs to backbone wired networks is presented in this paper. The AP adapts the QoS signalling of the connected networks. The AP has been implemented in Windows NT workstation as a protocol driver and its operation has been verified. The evaluation of the driver performance encourages the usage of the AP in demanding environments.

I and i

Abstract: 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 …

Machine learning

Abstract: 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.).

Survey of Wireless Indoor Positioning Techniques and Systems

Abstract: 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.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.