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.

Policy language based management model for wireless networks

Abstract: In the future all network, service management and control tasks can be policy based. As the networks and services become more and more complex new multilevel and hierarchical control methods are needed. Policy based management is a new and effective way to implement the needed hierarchical control model for multiservice and network domain environment. By defining the framework and ruling for a domain interconnection we can create a quite simple but very efficient control model for a large and diverse network and service environment. XML language standardization combined with policy management architecture forms a very powerful and flexible framework for future network management. Our goal has been to define and develop a complete and integrated policy based management system based on XML standardization for future 3/4G networks and systems.

On the credibility of load-latency measurement of network-on-chips

Abstract: This paper studies the impact of various simulation and network-on-chip (NoC) setups in common load-latency curves that are used for performance evaluation. The different setups yield very large variation in the observed performance yet they are too often undocumented. Vague definitions make the comparison of NoCs hard or impossible since the large uncertainties hide the actual differences between compared networks. Hence, this paper presents guidelines for performing load-latency measurements for network-on-chips to avoid these pitfalls.

Frequency management tool for multi-cell WLAN performance optimization

Abstract: Careful configuration of frequencies for WLAN access points (AP) is crucial for ensuring the optimal performance of the network. Thus, advanced tools for WLAN frequency management tasks are needed. This paper presents a practical tool for the frequency management in IEEE 802.11 WLANs. The tool minimizes the interference between APs and consequently maximizes the effective capacity of the network. It also contains a graphical user interface (GUI) showing an illustrative view of the network state in frequency domain. The tool has been designed for administrators of medium and large WLAN networks, such as used in companies, airports, and campus areas. In the presented case, the throughput of the optimized network was 60 % higher compared to the original network with random channels

Reconfiguration mechanism for an IP block based interconnection

Abstract: This paper presents a reconfiguration mechanism for an on-chip interconnection scheme called Heterogeneous IP Block Interconnection (HIBI). Required memory structures and logical signal operations for the different configurations are explained. The possible applications for this kind of reconfiguration are discussed, including ways to enhance system performance, ease of design re-use, low power designs and fault tolerance. An overview of HIBI is given as a background information for the reader. The HIBI architecture is designed to exploit VHDL synthesis but the concept could conceivably be transferred to any synthesis environment.

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.