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.

Energy efficient wireless sensor network, node devices for the same and a method for arranging communications in a wireless sensor network

Abstract: A wireless sensor network, a node device thereof and a method for arranging communications therein are presented. A first frequency is used in wireless communication of information between a headnode and subnodes of a first cluster (103) using a time slotted channel access scheme. A headnode of a second cluster (113) known the first frequency and selects a second, different frequency for use in wireless communication of information within said second cluster (113) using a time slotted channel access scheme. The headnode of the first cluster (103) is informed about the second frequency selected for the second cluster (113). Information from the headnode of said first cluster (103) to the headnode of said second cluster (113) is communicated on said second frequency, using the same time slotted channel access scheme as other nodes in said second cluster (113).

Performance analysis of IEEE 802.15.4 and ZigBee for large-scale wireless sensor network applications

Abstract: This paper analyses the performance of IEEE 802.15.4 Low-Rate Wireless Personal Area Network (LR-WPAN) in a large-scale Wireless Sensor Network (WSN) application. To minimize the energy consumption of the entire network and to allow adequate network coverage, IEEE 802.15.4 peer-to-peer topology is selected, and configured to a beacon-enabled cluster-tree structure. The analysis consists of models for CSMA-CA mechanism and MAC operations specified by IEEE 802.15.4. Network layer operations in a cluster-tree network specified by ZigBee are included in the analysis. For realistic results, power consumption measurements on an IEEE 802.15.4 evaluation board are also included. The performances of a device and a coordinator are analyzed in terms of power consumption and goodput. The results are verified with simulations using WIreless SEnsor NEtwork Simulator (WISENES). The results depict that the minimum device power consumption is as low as 73 μW, when beacon interval is 3.93 s, and data are transmitted at 4 min intervals. Coordinator power consumption and goodput with 15.36 ms CAP duration and 3.93 s beacon interval are around 370 μW and 34 bits/s

Experiments on local positioning with Bluetooth

Abstract: This paper presents the design and implementation of the Bluetooth local positioning application. Positioning is based on received power levels, which are converted to distance estimates according to a simple propagation model. The extended Kalman filter computes a 3D position estimate on the basis of distance estimates. With the used Bluetooth hardware, the mean absolute error of positioning was measured to be 3.76 m. The accuracy can be improved if Bluetooth devices are able to measure received power levels more precisely.

UML-based multiprocessor SoC design framework

Abstract: This paper describes a complete design flow for multiprocessor systems-on-chips (SoCs) covering the design phases from system-level modeling to FPGA prototyping. The design of complex heterogeneous systems is enabled by raising the abstraction level and providing several system-level design automation tools. The system is modeled in a UML design environment following a new UML profile that specifies the practices for orthogonal application and architecture modeling. The design flow tools are governed in a single framework that combines the subtools into a seamless flow and visualizes the design process. Novel features also include an automated architecture exploration based on the system models in UML, as well as the automatic back and forward annotation of information in the design flow. The architecture exploration is based on the global optimization of systems that are composed of subsystems, which are then locally optimized for their particular purposes. As a result, the design flow produces an optimized component allocation, task mapping, and scheduling for the described application. In addition, it implements the entire system for FPGA prototyping board. As a case study, the design flow is utilized in the integration of state-of-the-art technology approaches, including a wireless terminal architecture, a network-on-chip, and multiprocessing utilizing RTOS in a SoC. In this study, a central part of a WLAN terminal is modeled, verified, optimized, and prototyped with the presented framework.

A High-Performance Sum of Absolute Difference Implementation for Motion Estimation

Abstract: This paper presents a high-performance sum of absolute difference (SAD) architecture for motion estimation, which is the most time-consuming and compute-intensive part of video coding. The proposed architecture contains novel and efficient optimizations to overcome bottlenecks discovered in existing approaches. In addition, designed sophisticated control logic with multiple early termination mechanisms further enhance execution speed and make the architecture suitable for general-purpose usage. Hence, the proposed architecture is not restricted to a single block-matching algorithm in motion estimation, but a wide range of algorithms is supported. The proposed SAD architecture outperforms contemporary architectures in terms of execution speed and area efficiency. The proposed architecture with three pipeline stages, synthesized to a 0.18-mum CMOS technology, can attain 770-MHz operating frequency at a cost of less than 5600 gates. Correspondingly, performance metrics for the proposed low-latency 2-stage architecture are 730 MHz and 7500 gates

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.