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.

Revenue-maximization-based adaptive WFQ

Abstract: In the future Internet, di erent applications such as Voice over IP (VoIP) and Video-on-Demand (VoD) arise with di erent Quality of Service (QoS) parameters including e.g. guaranteed bandwidth, delay jitter, and latency. Different kinds of service classes (e.g. gold, silver, bronze) arise. The customers of di erent classes pay di erent prices to the service provider, who must share resources in a plausible way. In a router, packets are queued using a multi-queue system, where each queue corresponds to one service class. In this paper, an adaptive Weighted Fair Queue based algorithm for traAEc allocation is presented and studied. The weights in gradient type WFQ algorithm are adapted using revenue as a target function.

PC/104 based wireless diagnostics module

Abstract: A diagnostics systems module based on PC/104 computer platform standard has been developed A wireless diagnostics module is battery operated and connected to a diagnostics access point using IEEE 80211b wireless local area network (WLAN) link The diagnostics module performance is evaluated in the means of power consumption and wireless link capacity The results show that the power consumption of a PC/104 diagnostics module is high for battery operating systems However, it can be significantly reduced by component selection The IEEE 80211b wireless link performance is adequate for enabling diagnostics applications, but transfer delays can be significant with high network traffic load The PC/104 architecture is found to be suitable for industrial use The architecture can be easily implemented and changed for other types of applications

High-level synthesized 2-D IDCT/IDST implementation for HEVC codecs on FPGA

Abstract: This paper presents efficient inverse discrete cosine transform (IDCT) and inverse discrete sine transform (IDST) implementations for High Efficiency Video Coding (HEVC). The proposal makes use of high-level synthesis (HLS) to implement a complete HEVC 2-D IDCT/IDST architecture directly from the C code of a well-known Even-Odd decomposition algorithm. The final architecture includes a 4-point IDCT/IDST unit for the smallest transform blocks (TB), an 8/16/32-point IDCT unit for the other TBs, and a transpose memory for intermediate results. On Arria II FPGA, it supports real-time (60 fps) HEVC decoding of up to 2160p format with 12.4 kALUTs and 344 DSP blocks. Compared with the other existing HLS approach, the proposed solution is almost 5 times faster and is able to utilize available FPGA resources better.

Security Assessment of a Distributed, Modbus-Based Building Automation System

Abstract: Building automation systems were designed in an era when security was not a concern as the systems were closed from outside access. However, multiple benefits can be found in connecting such systems over the Internet and controlling a number of buildings from a single location. Security breaches towards building automation systems are increasing and may cause direct or indirect damages to the target organization or even the residents of the building. This work presents an approach to apply a method of data flow recognition and environment analysis to building automation through a case study on a distributed building automation system utilizing the Modbus protocol at the sites and presents suggested methods for mitigating the risks.

Tool-aided design and implementation of indoor surveillance wireless sensor network

Abstract: This paper presents the design and implementation of an indoor surveillance Wireless Sensor Network (WSN) using tools for hastening and facilitating the different phases in the WSN development. First, the application case is described in WISENES (WIreless SEnsor NEtwork Simulator) framework by four models, which define application, communication, node, and environment. WISENES enables a graphical design of the models combined with accurate simulations for performance evaluation. Next, surveillance application tasks and communication protocols are implemented on node platforms on top of SensorOS Operating System (OS). A congruent programming model of SensorOS allows a straightforward mapping of WISENES models to the final implementation. The evaluation of the indoor surveillance WSN implemented with Tampere University of Technology WSN (TUTWSN) protocols and platforms reaches a lifetime in order of years while still ensuring reactive operation. Further, the results show only 9.5% and 6.6% differences in simulated and measured networking delay and power consumption, respectively. Our results indicate that accurate early design phase simulations can relieve the burden of prototyping and low level implementation by a realistic configuration evaluation during design time.

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.