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.

Compression Methods for Microclimate Data Based on Linear Approximation of Sensor Data

Abstract: Edge computing is currently one of the main research topics in the field of Internet of Things. Edge computing requires lightweight and computationally simple algorithms for sensor data analytics. Sensing edge devices are often battery powered and have a wireless connection. In designing edge devices the energy efficiency needs to be taken into account. Pre-processing the data locally in the edge device reduces the amount of data and thus decreases the energy consumption of wireless data transmission. Sensor data compression algorithms presented in this paper are mainly based on data linearity. Microclimate data is near linear in short time window and thus simple linear approximation based compression algorithms can achieve rather good compression ratios with low computational complexity. Using these kind of simple compression algorithms can significantly improve the battery and thus the edge device lifetime. In this paper linear approximation based compression algorithms are tested to compress microclimate data.

Design and implementation of a WLAN terminal using UML 2.0 based design flow

Abstract: This paper presents a UML 2.0 based design flow for real-time embedded systems. The flow starts with UML 2.0 application, architecture and mapping models for our TUTWLAN terminal with its medium access control protocol. As a result, the hardware/software implementation on Altera Excalibur FPGA is achieved. Implementation utilizes eCos real-time operating system, and hardware accelerators for time-critical protocol functions. The design flow is prototyped in practice showing rapid UML 2.0 application model modification, real-time protocol processing in an image transfer application, and execution monitoring.

Interfacing multiple processors in a system-on-chip video encoder

Abstract: Interfacing RISC and DSP processors as Intellectual Property blocks for an MPEG-4 baseline video encoder is presented. Our previously presented Heterogeneous IP Block Interconnection (HIBI) architecture is used as a base for contemporary System-on-Chip implementations. Cost effective, general-purpose processor cores and DSPs lacking a native HIBI support need very low-delay interfacing units that are explained in detail in this paper. Interfaces are written in synthesisable VHDL and verified in Mentor Seamless CVE co-verification environment.

Optimizing finite state machines for system-on-chip communication

Abstract: In this paper, finite state machine (FSM) optimization for a system-on-chip (SoC) interconnection is presented. In the used interconnection architecture, the same interface block is used repeatedly and, therefore, optimization of the interface for synthesis is a very critical implementation issue. However, low-level hand-optimization is not desirable and, therefore, optimization should be performed in the high-level description or automatically in the synthesis process. The results of this paper suggest that design space exploration leads to substantial improvements when constructing complex SoCs. Ideas on how to support this automatically with FSM optimization are shown.

Teaching system-on-chip design with FPGAs

Abstract: This paper presents our experiences in using FPGA in teaching System-on-Chip design in Tampere University of Technology. We had a major reform on our courses and, most notably, chose a common HW platform which is used in 11 courses. It has proved good that most exercises are mandatory and bonus points are awarded for good work. In order to manage the schedules, larger projects have been partitioned by the teachers into smaller tasks and pairwork is allowed. Automated testbenches, reuse, startup examples were very useful. As a result, we observed increased motivation among students and better learning outcomes. The schedule slippages were reduced, although both teachers and students still underestimate the required time and effort. Moreover, we introduce 15 student projects where FPGA platform was also used. Some of the most innovative topics were suggested by students themselves, such as games. In the future, more effort is needed is finalizing the project works for easier reuse and setting up a common repository.

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.