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Author

Timo Hämäläinen

Other affiliations: Dalian Medical University, Nokia, Dublin Institute of Technology  ...read more
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.


Papers
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Proceedings ArticleDOI
13 Apr 2008
TL;DR: A new way of implementing converged wireless network and service management system using XML as the management message language and SOAP/HTTP as the protocols for configuration, performance, fault and alarm communication between network operation and maitenance system and network elements and service provider's service production systems is described.
Abstract: This paper describes a new way of implementing converged wireless network and service management system using XML as the management message language and SOAP/HTTP as the protocols for configuration, performance, fault and alarm communication between network operation and maitenance (O&M) system and network elements (NE) and service provider's service production systems. Principles described in this article are fully tested and applicable and can be exploited in any current network and service management system.

1 citations

Proceedings ArticleDOI
10 Jun 2020
TL;DR: This paper considers setting up the development and run-time environments, selecting the proper analysis approach and evaluating the difficulty in five different aspects of Apache Flink, and lends the Degree of Difficulty (DoD) measure from sports to assess the deployment effort.
Abstract: We have used log file analysis in mining expected behavior in intelligent transportation systems involving spatial and temporal data. The challenge is how to extract complex behavior from multiple traces, in which linear log analysis proceeding in a row by row order does not suffice. Complex Event Processing (CEP) is close to our need, but it is surprisingly difficult to set up and deploy general purpose frameworks to the purpose. This paper originates from the need to compare our custom LOGDIG tool to Apache Flink. This paper focuses on the deployment effort of the two, for which reason we consider setting up the development and run-time environments, selecting the proper analysis approach and evaluating the difficulty in five different aspects. While LOGDIG is written solely in Python, Flink is a combination of many languages, libraries, packages and tools. Our comparison includes Flink in batch and stream processing modes using external and internal preprocessing. We lend the Degree of Difficulty (DoD) measure from sports to assess the deployment effort. Flink needs significant setup effort for deploying the same functionality as LOGDIG. The former is continuously developing while LOGDIG is more focused and stable and can be used more easily off-the-self.

1 citations

Proceedings ArticleDOI
05 Aug 1996
TL;DR: A secure infrared remote control system that matches several data encoding methods and carrier modulation techniques and is suitable for electronic door locking purposes for domestic and automotive applications.
Abstract: A secure infrared remote control system is presented. Offering flexible programmability, the presented system matches several data encoding methods and carrier modulation techniques. Arbitrary long data codes can be transmitted within the capacity of the system memory. The low cost of the implementation is due to commercial components and a minimum number of circuits on the small sized printed circuit board. Powered by a battery energy source, the presented system is suitable for electronic door locking purposes for domestic and automotive applications. An example application for a car security access control is presented. The system has been tested under variable environment conditions and found to be very reliable and robust.

1 citations

Proceedings Article
01 Jan 2012
TL;DR: The approach based on self-organizing maps is considered to detect online HTTP attacks in the case of continuous updated web-applications and almost all attacks from these logs have been detected.
Abstract: In modern networks HTTP clients request and send information using queries. Such queris are easy to manipulate to include malicious attacks which can allow attackers to corrupt a server or collect confidential information. In this study, the approach based on self-organizing maps is considered to detect such attacks. Feature matrices are obtained by applying n-gram model to extract features from HTTP requests contained in network logs. By learning on basis of these matrices, growing hierarchical self-organizing maps are constructed and by using these maps new requests received by the web-server are classified. The technique proposed allows to detect online HTTP attacks in the case of continuous updated web-applications. The algorithm proposed was tested using Logs, which were aquire acquired from a large real-life web-service and include normal and intrusive requests. As a result, almost all attacks from these logs have been detected, and the number of false alarms was very low at the same time.

1 citations

Journal ArticleDOI
TL;DR: In this paper , the authors formulated the joint data sensing and processing optimization problem to ensure the freshness of the status updates and reduce the energy consumption of IoT devices and proposed a multi-variable iterative system cost minimization algorithm to optimize the system overhead.
Abstract: IoT devices recently are utilized to detect the state transition in the surrounding environment and then transmit the status updates to the base station for future system operations. To satisfy the stringent timeliness requirement of the status updates for the accurate system control, age of information (AoI) is introduced to quantify the freshness of the sensory data. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution to ensure the information freshness. Since the status updates generated by insufficient sensing operations may be invalid and cause additional processing time, the data sensing and processing operations need to be considered simultaneously. In this work, we formulate the joint data sensing and processing optimization problem to ensure the freshness of the status updates and reduce the energy consumption of IoT devices. Then, the formulated NP-hard problem is decomposed into the sampling, sensing and computation offloading optimization problems. Afterwards, we propose a multi-variable iterative system cost minimization algorithm to optimize the system overhead. Simulation results show the efficiency of our method in decreasing the system cost and dominance of sensing and processing under different scenarios.

1 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Journal ArticleDOI
01 Nov 2007
TL;DR: Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
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.

4,123 citations

01 Jan 2006

3,012 citations

01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

2,933 citations