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.

Range-free algorithm for energy-efficient indoor localization in Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) form an attractive technology for ubiquitous indoor localization. The localized node lifetime is maximized by using energy-efficient radios and minimizing their active time. However, the most low-cost and low-power radios do not include Received Signal Strength Indicator (RSSI) functionality commonly used for RF-based localization. In this paper, we present a range-free localization algorithm for localized nodes with minimized radio communication and radios without RSSI. The low complexity of the algorithm enables implementation in resource-constrained hardware for in-network localization. We experimented the algorithm using a real WSN implementation. In room-level localization, the area was resolved correctly 96% of the time. The maximum point-based error was 8.70 m. The corresponding values for sub-room-level localization are 100% and 4.20 m. The prototype implementation consumed 1900 B of program memory. The data memory consumption varied from 18 B to 180 B, and the power consumption from 345 μW to 2.48 mW depending on the amount of localization data.

Adaptive scheduling method for maximizing revenue in flat pricing scenario

Abstract: End-to-end quality of service is critical to the success of current and future networked applications. Applications, such as real-time actions and transactions, should be given priority over less critical ones (such as web surfing). Furthermore, many multimedia applications require delay or delay variation guarantees for acceptable performance. Weighted fairness is also important both among customers or aggregates (depending on the tariff or subscription), and also within an aggregate (for example, to prevent starvation among sessions or service categories). This paper presents an adaptive scheduling algorithm for the traffic allocation. We use flat pricing scenario in our model, and the weights of the queues are updated using revenue as a target function. Due to the closed form nature of the algorithm, it can operate in the nonstationary environments. In addition, it is nonparametric and deterministic in the sense that any assumptions about connection density functions or duration distributions are not made.

Using a communication generator in SoC architecture exploration

Abstract: This paper presents an implementation of a communication generator, named transaction generator. It is utilized in communication-centric SoC architecture exploration where the objective is to find the optimal hardware allocation, task partitioning, and scheduling with a given application model and architectural requirements. An application is abstracted with a process network model of computation and architecture is described with characteristic metrics. In addition to accelerating architecture exploration, transaction generator can be used in development, verification and comparison of on-chip communication networks.

Parallel DSP implementation of wavelet transform in image compression

Abstract: A parallel implementation of the 2D discrete wavelet transform on a distributed memory multiprocessor system called PARNEU is presented. The mapping has been chosen with consideration to load balancing and communication methods in order to achieve the best possible scalability and performance in transforming one single image. Detailed performance figures are included. Experimental results show that significant parallel speedup is reached with this mapping.

Evaluating UML2 modeling of IP-XACT objects for automatic MP-SoC integration onto FPGA

Abstract: IP-XACT is a standard for describing intellectual property metadata for System-on-Chip (SoC) integration. Recently researchers have proposed visualizing and abstracting IP-XACT objects using structural UML2 model elements and diagrams. Despite the number of proposals at conceptual level, experiences on utilizing this representation in practical SoC development environments are very limited. This paper presents how UML2 models of IP-XACT features can be utilized to efficiently design and implement a multiprocessor SoC prototype on FPGA. The main contribution of this paper is the experimental development of a multiprocessor platform on FPGA using UML2 design capture, IP-XACT compatible components, and design automation tools. In addition, modeling concepts are improved from earlier work for the utilized integration methodology.

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.