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.

Mobility support and soft handover protocol for ip-networks

Abstract: In this paper, a handover mechanism that offers soft handover support between two different IP subnets for mobile clients is introduced. This handover is a part of a whole mobility support protocol consisting of several components. The handover is based on a protocol that introduces new methods for updating the location of mobile nodes. The handover is designed to cause no or minimal packet loss and be fast. It uses two different interfaces for achieving it.

Node scheduler optimization for different traffic classes with CAC enhancement

Abstract: In the current development of all IP networks different traffic classes, which are used for different services will share the same bandwidth resources of the network. For effective usage of the system resources, arrivals for some low priority traffic classes could be rejected in cases when risk of having overload is high. In this paper, the use of connection admission control (CAC) algorithm together with scheduler optimizing is considered. The scheduler is functioning according to the optimal weight allocation for each traffic class, and CAC is based on the current utilization of the scheduler. Also one traffic class, which is referenced as "voice" class has more priority compared to others. For the scheduler optimization, revenue function is considered with linear approximation and the adaptive weights are found based on Lagrangian relaxation. To show the accuracy of the use of CAC algorithm, simulation results are presented and analyzed.

Real-time implementation of H.263 video encoder on TMS320C6201 fixed point DSP

Abstract: The real-time implementation of H.263 video encoder following ITU-T H.263 recommendation is described. [1] The current implementation shows that QCIF-size images can be encoded in real-time by the TMS320C6201 when computationally light motion estimation algorithm and basic H.263 coding mode is used. The real-time performance can be achieved using C-code and optimizing some key functions with assembler. In addition, careful memory management design for program code and application data is required. With presented coding scheme, up to 31 fps can be achieved.

Performance Evaluation of OpenFlow Enabled Commodity and Raspberry pi Wireless Routers

Abstract: Software defined network (SDN) allows the decoupling of data and control plane for dynamic and scalable network management. SDN is usually associated with OpenFlow protocol which is a standard interface that enables the network controllers to determine the path of network packets across a network of switches. In this paper, we evaluate openflow performance using commodity wireless router and raspberry pi with two different SDN controllers. Our test setup consists of wired and wireless client devices connected to openflow enabled commodity wireless router and raspberry pi. All clients used traffic generator tool to transmits data to a sink server host. The results are promising and paves the way for further research on using software defined wireless network.

Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks

Abstract: The sharing of high-quality traffic information plays a crucial role in enhancing the driving experience and safety performance for vehicular networks, especially in the development of electric vehicles (EVs). The crowdsourcing-based real-time navigation of charging piles is characterized by low delay and high accuracy. However, due to the lack of an effective incentive mechanism and the resource-consuming bottleneck of sharing real-time road conditions, methods to recruit or motivate more EVs to provide high-quality information gathering has attracted considerable interest. In this paper, we first introduce a blockchain platform, where EVs act as the blockchain nodes, and a reputation-based incentive mechanism for vehicular networks. The reputations of blockchain nodes are calculated according to their historical behavior and interactions. Further, we design and implement algorithms for updating honest-behavior-based reputation as well as for screening low-reputation miners, to optimize the profits of miners and address spatial crowdsourcing tasks for sharing information on road conditions. The experimental results show that the proposed reputation-based incentive method can improve the reputation and profits of vehicle users and ensure data timeliness and reliability.

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.