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.

Bandwidth allocation and pricing in multinode network

Abstract: This paper presents adaptive resource sharing model that uses a revenue criterion to allocate network resources in the optimal way. The model ensures QoS requirements of data flows and, at the same time, maximizes the total revenue by adjusting parameters of the underlying scheduler. Besides, the adaptive model eliminates the need to find the optimal static weight values because they are calculated dynamically. The simulation consists of several cases that analyse the model and the way it provides the required QoS guarantees. The simulation reveals that the installation of the adaptive model increases the total revenue and ensures the QoS requirements for all service classes.

H.263 video encoder implementation on a scalable DSP system

Abstract: A mapping and realization of H.263 video encoder for video conferencing applications are given using our DSP based multiprocessor system, called PAKNEU. PARNEU system is built for computationally intensive applications like image and video processing as well as soft computing applications. PARNEU has flexible communication architecture and thus it allows different mapping possibilities. The presented data parallel mapping has low communication and memory requirements, which allows encoding of any of the five standard H.263 picture formats. With a prototype system using four ADSP-21062 DSPs, a real-time encoding is achieved with QCIF sized picture. Phase times for each step of H.263 encoder are presented.

Cybersecurity Safeguards for the Automotive Emergency Response Vehicles.

Abstract: A modern Emergency Response Vehicle (ERV) is a combination of emergency services and functional mobile office on the wheels. Researchers have observed that emergency response personnel including Law Enforcement Authorities (LEAs), Police and border guards, could be on the duty while having possibility to use same services compared to fixed office. During our research study, users have registered special demand for mobile office. To meet this demand, designers and engineers have combined a modern vehicle platform with computers, monitors, wireless connectivity and many other devices needed in everyday activities. However, there is continue challenge, the standards are not covering information and cyber security properly. This research paper fulfils that gap by applying the Finnish National Security Auditing Criteria version 2 (KATAKRI II) on eight asset classes that have recognised in Mobile Object Bus Integration (MOBI) project. The outcome is a pragmatic solution that provides large set of safeguards for guaranteeing ERV information

Towards a Unified Cybersecurity Testing Lab for Satellite, Aerospace, Avionics, Maritime, Drone (SAAMD) technologies and communications

Abstract: Aviation, maritime, and aerospace traffic control, radar, communication, and software technologies received increasing attention in the research literature over the past decade, as software-defined radios have enabled practical wireless attacks on communication links previously thought to be unreachable by unskilled or low-budget attackers. Moreover, recently it became apparent that both offensive and defensive cybersecurity has become a strategically differentiating factor for such technologies on the war fields (e.g., Ukraine), affecting both civilian and military missions regardless of their involvement. However, attacks and countermeasures are usually studied in simulated settings, thus introducing the lack of realism or non-systematic and highly customized practical setups, thus introducing high costs, overheads, and less reproducibility. Our"Unified Cybersecurity Testing Lab"seeks to close this gap by building a laboratory that can provide a systematic, affordable, highly-flexible, and extensible setup. In this paper, we introduce and motivate our"Unified Cybersecurity Testing Lab for Satellite, Aerospace, Avionics, Maritime, Drone (SAAMD)"technologies and communications, as well as some peer-reviewed results and evaluation of the targeted threat vectors. We show via referenced peer-reviewed works that the current modules of the lab were successfully used to realistically attack and analyze air-traffic control, radar, communication, and software technologies such as ADS-B, AIS, ACARS, EFB, EPIRB and COSPAS-SARSAT. We are currently developing and integrating support for additional technologies (e.g., CCSDS, FLARM), and we plan future extensions on our own as well as in collaboration with research and industry. Our"Unified Cybersecurity Testing Lab"is open for use, experimentation, and collaboration with other researchers, contributors and interested parties.

Predictive pumping based on sensor data and weather forecast

Abstract: In energy production, peat extraction has a significant role in Finland. However, protection of nature has become more and more important globally. How do we solve this conflict of interests respecting both viewsƒ In peat production, one important phase is to drain peat bog so that peat production becomes available. This means that we have control over how we can lead water away from peat bog to nature without water contamination with solid and other harmful substances. In this paper we describe a novel method how fouling of water bodies from peat bog can be controlled more efficiently by using weather forecast to predict rainfall and thus, minimize the effluents to nature.

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.