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.

Interference Cancellation Schemes for Spread Spectrum Systems with Blind Principles

Abstract: Employing a Blind Source Separation (BSS) algorithm is one of the mechanisms used in extracting unobserved signals from observed mixtures in signal processing. Direct Sequence - Code Division Multiple Access (DS-CDMA) is mature and prominent in spreading code assisted spread spectrum based multiple access communication techniques. Mitigation of deteriorative effects caused within the air interface of DS-CDMA is aimed by trying to remove the jamming signal. A pair of algorithms derived with the aid of BSS schemes are presented in this paper. In the short code model time correlation properties of the channel is taken advantage for BSS. Two energy functions of receive signal are used with the iterative fixed point rule in determining the filter coefficients. The methods are tested in a downlink channel. Equal Gain Combining (EGC) is used to treat the channel parameter values. They are important mechanisms due to simplicity and applicability to High Speed Packet Access (HSPA) based systems.

Architecture for a Windows NT wireless LAN multimedia terminal

Abstract: The support for multimedia services in wireless access networks is challenging to implement, due to an unreliable and bandwidth-limited wireless medium. In addition, the widely used applications and protocols generally lack the support for quality-of-service (QoS) parameters. This paper presents the architecture of a multimedia wireless LAN terminal. The terminal is implemented using a custom network demonstrator platform connected to a Windows NT workstation. The system provides a separate management plane for configuring the service parameters of a proprietary wireless medium access control (MAC) protocol. Also, native applications that are capable of accessing the MAC QoS parameters directly are supported.

Performance evaluation of Secure Remote Password protocol

Abstract: The paper evaluates the performances of the Secure Remote Password (SRP) authentication protocol computations. The software implementations are written in C with the MIRACL and OpenSSL libraries utilized and evaluated on Pentium III and ARM9TDMI microprocessors. Accelerating the performance of the critical computational parts with dedicated hardware is discussed. The measurements show that with a prime modulus of length 2048 bits the SRP computations take 80.5 ms on 700 MHz Pentium III and 503 ms on 200 MHz ARM9TDMI with the highest software optimizations within the libraries. With maximal precomputation the execution times can be decreased down to 30.6 ms and 194 ms respectively. By adding appropriate hardware support for the SHA-1 hash computation and exponentiation their cycle counts can be reduced at least by factors of 10 and 20.

Revenue and delay control in 3G service network

Abstract: In the future, 3G networks will be required to provide support for a number of different types of traffic and services, each with its own particular characteristics and quality of service parameters, including e.g. guaranteed bandwidth, jitter, and latency. In this paper, an adaptive weighted fair queue (WFQ) based algorithm for 3GPP traffic class allocation is presented and studied in the single node case. This node can be any RAN or core network user plane node. The weights in the fixed-point type fast adaptive WFQ algorithm are updated using revenue as a target function. Instead of revenue, also delay or throughput could be used as targets. Due to the adaptive nature of the algorithm, it can operate in non-stationary environments. In addition, it is nonparametric and deterministic in the sense that no assumptions about call density functions or duration distributions are made.

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.