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.

Software support for low latency communication in scalable multimedia DSP system

Abstract: The software development and optimization of the communication is presented for an experimental scalable multiprocessor system purposed for multimedia applications. For convenient application mappings, presented C-code primitives hide the communication topology and allow the system to be expanded without needs to rewrite programs. For real-time applications, fast hardware communication need a strong support from software to prevent large latency times. Measured latency times and obtained throughput in communication are compared between user level application and maximum available hardware performance.

Design of a manageable WLAN Access Point

Abstract: This paper presents the design and prototype implementation of a manageable WLAN Access Point (mAP). mAP has been developed for managing WLAN Quality of Service (QoS), frequency selection, client configuration, and for collecting a wide range of management information. The prototype is implemented using a Linux platform. With the presented architecture, the mAP functionality can be easily extended by adding new management functions and automated services.

On the asymptotically optimal tuning of robust controllers for systems in the CD-algebra

Abstract: The authors previously (2000) showed that a low-gain controller of the form C/sub /spl epsiv//(s)=/spl Sigma//sub k=-n//sup n/ /spl epsiv/K/sub k//(s-i/spl omega//sub k/) is able to track and reject constant and sinusoidal reference and disturbance signal for a stable plant in the Callier-Desoer (CD) algebra. In this note, we investigate the optimal tuning of the matrix gains K/sub k/ of the controller C/sub /spl epsiv//(s) as the scalar gain /spl epsiv//spl darr/0. The cost function is the maximum error between the reference signal and the measured output signal over all frequencies and bounded reference and disturbance signal amplitudes. Closed forms for asymptotically globally optimal solutions are given. The optimal matrix gains K/sub k/ are expressed in terms of the values of the plant transfer matrix at the reference and disturbance signal frequencies. Thus the matrices K/sub k/ can be tuned with input-output measurements made from the open loop plant without knowledge of the plant model. Although the analysis is in the CD-algebra, to the authors' knowledge the main results are new even for finite-dimensional systems.

Scalable DSP implementation of DCT-based motion estimation algorithm

Abstract: A parallel implementation of a DCT-based motion estimation algorithm (DXT-ME) for 2D images/signals is presented for a parallel scalable DSP system called PARNEU. PARNEU was used to test the performance of the parallelism. The DCT-based motion estimation can be used for video coding instead of the more frequently used full search block-matching approach (BKM-ME). The DCT-based system has lower computational complexity compared to the BKM-ME and it can result in a higher system throughput. Data parallel implementation scales very well and the performance measurements are promising compared to the traditional methods.

Cybersecurity of COSPAS-SARSAT and EPIRB: threat and attacker models, exploits, future research

Abstract: COSPAS-SARSAT is an International programme for"Search and Rescue"(SAR) missions based on the"Satellite Aided Tracking"system (SARSAT). It is designed to provide accurate, timely, and reliable distress alert and location data to help SAR authorities of participating countries to assist persons and vessels in distress. Two types of satellite constellations serve COSPAS-SARSAT, low earth orbit search and rescue (LEOSAR) and geostationary orbiting search and rescue (GEOSAR). Despite its nearly-global deployment and critical importance, unfortunately enough, we found that COSPAS-SARSAT protocols and standard 406 MHz transmissions lack essential means of cybersecurity. In this paper, we investigate the cybersecurity aspects of COSPAS-SARSAT space-/satellite-based systems. In particular, we practically and successfully implement and demonstrate the first (to our knowledge) attacks on COSPAS-SARSAT 406 MHz protocols, namely replay, spoofing, and protocol fuzzing on EPIRB protocols. We also identify a set of core research challenges preventing more effective cybersecurity research in the field and outline the main cybersecurity weaknesses and possible mitigations to increase the system's cybersecurity level.

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.