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.

Leveraging National Auditing Criteria to Implement Cybersecurity Safeguards for the Automotive Emergency Response Vehicles : A case study from Finland

Abstract: A modern Emergency Response Vehicle (ERV) is a combination of emergency services and functional mobile office on the wheels. The mobile office is aiming to leverage the benefits of fixed office while moving 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. On the one hand, demand of mobile office has significantly improved the emergency response services. On the other hand, emergency vehicle designers should rethink the demand of users. This resulted into modern standard emergency response vehicle with three compartments including cabin, office space and transport space. 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. A set of standards has been released by local and global organisations to help building standard vehicle for emergency responses. 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 top of eight asset classes that have recognised in Mobile Object Bus Integration (MOBI) project. The outcome is a pragmatic proposal that provides set of safeguards for guaranteeing ERV information and cyber security. This paper presents the information and cyber security safeguards utilising standards presented in Finnish National Auditing Criteria and applying in emergency response vehicles.

Performance of OFDM with Blind Interference Suppression Schemes

Abstract: In statistical wireless signal processing, extraction of unobserved signals from observed mixtures can be achieved through Blind Source Separation (BSS) algorithms. Orthogonal Frequency Division Multiplexing (OFDM) can be pronounced as one of the predominant multicarrier air interface communication technique. Consequence of an effort taken to diminish the undesirable influences encountered with in the wireless interface of an OFDM system with the help of a BSS scheme based counteractive solution is disclosed by this paper. Filter coefficients that are employed at the receiver are ascertained with the support of energy functions and the iterative fixed point rule. Time correlation properties of the channel are taken advantage for BSS. Performance of the algorithm when it is used for an OFDM model is evaluated under slow fading channel conditions with a receiver containing Equal Gain Combining (EGC). The importance is that, this scheme can be lead to low complexity mechanisms.

Live Demonstration: Kvazzup 4K HEVC Video Call

Abstract: This paper describes a demonstration setup for an end-to-end 4K video call with Kvazzup open-source HEVC video call application. The Kvazzup clients are installed on a desktop and a laptop computer powered by Intel 22-core Xeon and Intel 4-core i7 processors, respectively. The proposed two-way peer-to-peer video call setup is shown to support 2160p30 video stream from the desktop to the laptop and 720p30 stream in the reverse direction.

Machine Learning Models for Predicting Adverse Pregnancy Outcomes in Pregnant Women with Systemic Lupus Erythematosus

Abstract: Predicting adverse outcomes is essential for pregnant women with systemic lupus erythematosus (SLE) to minimize risks. Applying statistical analysis may be limited for the small sample size of childbearing patients, while the informative medical records could be provided. This study aimed to develop predictive models applying machine learning (ML) techniques to explore more information. We performed a retrospective analysis of 51 pregnant women exhibiting SLE, including 288 variables. After correlation analysis and feature selection, six ML models were applied to the filtered dataset. The efficiency of these overall models was evaluated by the Receiver Operating Characteristic Curve. Meanwhile, real-time models with different timespans based on gestation were also explored. Eighteen variables demonstrated statistical differences between the two groups; more than forty variables were screened out by ML variable selection strategies as contributing predictors, while the overlap of variables were the influential indicators testified by the two selection strategies. The Random Forest (RF) algorithm demonstrated the best discrimination ability under the current dataset for overall predictive models regardless of the data missing rate, while Multi-Layer Perceptron models ranked second. Meanwhile, RF achieved best performance when assessing the real-time predictive accuracy of models. ML models could compensate the limitation of statistical methods when the small sample size problem happens along with numerous variables acquired, while RF classifier performed relatively best when applied to such structured medical records.

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.