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.

Overview of research efforts on media ISA extensions and their usage in video coding

Abstract: This paper summarizes the results of over 25 research groups or individual researchers that have presented video coding implementations on general-purpose processors with the new single instruction multiple data media instruction set architecture extensions. The extensions are introduced and the fundamentals for extensions, as well as some inherent problems, are explained. The reported attempts to utilize the extensions are divided into kernel- and application-level, as well as platform dependent and independent optimizations. Optimized applications include, in addition to some proprietary methods, all of the major video coding standards such as H.261, H.263, MPEG-4, MPEG-1, and MPEG-2. These optimized implementations include a complete video codec, several decoders, and several encoders. Additionally, a performance comparison is given for four representative encoder implementations based on the reported results. Also included is an overview of future trends for new instructions and architectural speed-up techniques.

Low-Power Wireless Sensor Networks

Abstract: Wireless sensor network (WSN) is an ad-hoc network technology comprising even thousands of autonomic and self-organizing nodes that combine environmental sensing, data processing, and wireless networking. The applications for sensor networks range from home and industrial environments to military uses. Unlike the traditional computer networks, a WSN is application-oriented and deployed for a specific task. WSNs are data centric, which means that messages are not send to individual nodes but to geographical locations or regions based on the data content. A WSN node is typically battery powered and characterized by extremely small size and low cost. As a result, the processing power, memory, and energy resources of an individual sensor node are limited. However, the feasibility of a WSN lies on the collaboration between the nodes. A reference WSN node comprises a Micro-Controller Unit (MCU) having few Million Instructions Per Second (MIPS) processing speed, tens of kilobytes program memory, few kilobytes data memory. In addition, the node contains a short-range radio, and a set of sensors. Supply power is typically obtained with small batteries. Assuming a target lifetime of one year using AA-size batteries, the available power budget is around 1 mW. This book covers the low-power WSNs services ranging from hardware platforms and communication protocols to network deployment, and sensor data collection and actuation. The implications of resource constraints and expected performance in terms of throughput, reliability and latency are explained. As a case study, this book presents experiments with low-energy TUTWSN technology to illustrate the possibilities and limitations of WSN applications.

A parallel MPEG-4 encoder for FPGA based multiprocessor SoC

Abstract: A parallel MPEG-4 simple profile encoder for FPGA based multiprocessor system-on-chip (SoC) is presented. The goal is a computationally scalable framework independent of platform. The scalability is achieved by spatial parallelization where images are divided to horizontal slices. Slice coding tasks are mapped to the multiprocessor consisting of four soft-cores arranged into master-slave configuration. Also, the shared memory model is adopted where large images are stored in shared external memory while small on-chip buffers are used for processing. The interconnections between memories and processors are realized with our HIBI network. Our main contributions are the scalable encoder framework as well as methods for coping with limited memory of FPGA. The current software only implementation processes 6 QCIF frames/s with three encoding slaves. In practice, speed-ups of 1.7 and 2.3 have been measured with two and three slaves, respectively. FPGA utilization of current implementation is 59% requiring 24 207 logic elements on Altera Stratix EP1S40.

HIBI Communication Network for System-on-Chip

Abstract: This paper presents a communication network targeted for complex system-on-chip (SoC) and network-on-chip (NoC) designs. The Heterogeneous IP Block Interconnection (HIBI) aims at maximum efficiency and minimum energy per transmitted bit combined with quality-of-service (QoS) in transfers. Other features include support for hierarchical topologies with several clock domains, flexible scalability, and runtime reconfiguration of network parameters. HIBI is intended for integrating coarse-grain components such as intellectual property (IP) blocks that have size of thousands of gates.HIBI has been implemented in VHDL and SystemC and synthesized on several CMOS technologies and on FPGA. A 32-bit wrapper requires 5400 gates and runs with 315 MHz on 0.18 μ m technology which shows that only minimal area overhead is paid for the advanced features. The area and frequency results are well comparable to other NoC proposals.Furthermore, data transfers are shown to approach the maximum theoretical performance for protocol efficiency. HIBI network is accompanied with a design framework with tools for optimizing the system through automated design space exploration.

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.