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.

Modeling RISC-V Processor in IP-XACT

Abstract: IP-XACT is the most used standard in IP (Intellectual Property) integration. It is intended as a language neutral golden reference, from which RTL and HW dependent SW is automatically generated. Despite its wide popularity in the industry, there are practically no public and open design examples for any part of the design flow from IP-XACT to synthesis. One reason is the difficulty of creating IP-XACT models for existing RTL projects. In this paper, we address the issues by modeling the PULPino RISC-V microprocessor that is written in SystemVerilog (SV) and the project distributed over several repositories. We propose how to solve the mismatching concepts between SV project and IP-XACT, and based on the findings propose improvements for the Kactus2 IP-XACT tool. In addition, the final PULPino model contributes to the rare public non-trivial examples for better adoption of the IP-XACT methodology.

Delay performance measurements in multimedia switching

Abstract: A multimedia service connection is usually composed of several components, e.g., video, voice, data and control. Characteristics of these tributaries may vary substantially and thus it has been difficult to develop comprehensive analytical models to estimate, e.g., delay or bit error ratio performance. Additionally, queuing at various network nodes changes characteristics of the bit streams leading to increase of traffic burstiness. Due to the bursty nature of multimedia traffic, buffering easily becomes a critical issue consequently affecting delay performance. Since no adequate models for delay estimation are available, this paper approaches the performance problem by carrying out measurements in studying delay performance in multimedia switching nodes. MMPP (Markow-Modulated Poisson Process) distribution was chosen to represent performance and bursty nature of multimedia traffic.

Complexity based test cases for log file analyzers

Abstract: Log files are used in many big data applications. If the log is meant for a different purpose, the analysis and finding the best log analyzer can be very complex. Our solution is to create a generic test case framework to model and create representative log data. Related work model the behavior as state machines, but our model uses a composition of elementary acyclic graphs, thus addressing the log file size, variation, branching and confidence. We have created test cases originally based on real Intelligent Transportation Systems (ITS) data, and evaluated our LOGDIG analyzer against it. We can easily generate hundreds of test cases with our model, and modify the cases as needed.

On the structure of robust controllers for infinite-dimensional systems

Abstract: Starting from a very general formulation of the Internal Model Principle it is shown that a robust controller tracking/rejecting signals generated by an exosystem can be decomposed into a servocompensator and a stabilizing controller. The servocompensator contains an internal model of the exosystem generating the reference and disturbance signals and the stabilizing controller stabilizes the infinite-dimensional closed-loop system.

Co-simulation ofWireless Local Area Network Terminals with Protocol Software Implemented in SDL

Abstract: This paper presents the verification of our WLAN terminal (TUTWLAN), with its medium access control protocol and test applications, using cycle-accurate hardware/ software co-simulation. The protocol software has been implemented using SDL and automatic C code generation. The hardware implementation of the terminal contains hardware accelerators for time-critical protocol functions. Full system co-simulations were used for both the functional verification and performance evaluation of a single TUTWLAN terminal as well as a network of terminals. With simulations, the performance bottlenecks were identified, and the results enable the implementing of the next generation TUTWLAN terminal as a single-chip.

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.