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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
02 Dec 2013
TL;DR: This paper presents an IP-XACT based design flow that reduces the design time to one third compared to the conventional FPGA flow, the number of automated design phases is doubled and any manual error prone data transfer between HW and SW tools is completely avoided.
Abstract: Typical MPSoC FPGA product design is a rigid waterfall process proceeding one-way from HW to SW design. Any changes to HW trigger the SW project re-creation from the beginning. When several product variations or speculative development time exploration is required, the disk bloats easily with hundreds of Board Support Package (BSP), configuration and SW project files. In this paper, we present an IP-XACT based design flow that solves the problems by agile re-use of HW and SW components, automation and single golden reference source for information. We also present new extensions to IP-XACT since the standard lacks SW related features. Three use cases demonstrate how the BSP is changed, an application is moved to another processor and a function is moved from SW implementation to a HW accelerator. Our flow reduces the design time to one third compared to the conventional FPGA flow, the number of automated design phases is doubled and any manual error prone data transfer between HW and SW tools is completely avoided.

12 citations

Proceedings ArticleDOI
01 Jul 2006
TL;DR: The proposed novel parallel memory implementation allows conflict free accesses with all the constant strides which has not been possible in prior application specific parallel memories.
Abstract: Parallel memory modules can be used to increase memory bandwidth and feed a processor with only necessary data Arbitrary stride access capability with interleaved memories is described in previous research where the skewing scheme is changed at run time according to the currently used stride This paper presents the improved schemes which are adapted to parallel memories The proposed novel parallel memory implementation allows conflict free accesses with all the constant strides which has not been possible in prior application specific parallel memories Moreover, the possible access locations are unrestricted and the data patterns have equal amount of accessed data elements as the number of memory modules Timing and area estimates are given for Altera Stratix FPGA and 018 micrometer CMOS process with memory module count from 2 to 32 The FPGA results show 129 MHz clock frequency for a system with 16 memory modules when read and write latencies are 3 and 2 clock cycles, respectively

12 citations

Proceedings ArticleDOI
14 Dec 2003
TL;DR: Mechanisms that will enable network service providers to optimize their operations and make them economically viable, and to provide sufficient attractions to users of network services that will help smooth spikes in demand and lead to a more efficient utilization of the available resources are explored.
Abstract: This paper explores mechanisms that will enable network service providers to optimize their operations and make them economically viable, and to provide, through pricing, sufficient attractions to users of network services that will help smooth spikes in demand and lead to a more efficient utilization of the available resources Proposed model for charging and traffic allocation is suitable for an integrated service network

12 citations

Proceedings ArticleDOI
01 Dec 2019
TL;DR: This paper proposes a threat detection system based on Machine Learning classifiers that are trained using darknet traffic that can easily distinguish between benign and malign traffic and are able to detect known and unknown threats effectively with an accuracy above 99%.
Abstract: This paper proposes a threat detection system based on Machine Learning classifiers that are trained using darknet traffic. Traffic destined to Darknet is either malicious or by misconfiguration. Darknet traffic contains traces of several threats such as DDoS attacks, botnets, spoofing, probes and scanning attacks. We analyse darknet traffic by extracting network traffic features from it that help in finding patterns of these advanced threats. We collected the darknet traffic from the network sensors deployed at SURFnet and extracted several network-based features. In this study, we proposed a framework that uses supervised machine learning and a concept drift detector. Our experimental results show that our classifiers can easily distinguish between benign and malign traffic and are able to detect known and unknown threats effectively with an accuracy above 99%.

12 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