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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
05 Mar 2017
TL;DR: This paper presents the first known high-level synthesis (HLS) implementation of integer discrete cosine transform and discrete sine transform for High Efficiency Video Coding (HEVC) using a well-known row-column and Even-Odd decomposition techniques.
Abstract: This paper presents the first known high-level synthesis (HLS) implementation of integer discrete cosine transform (DCT) and discrete sine transform (DST) for High Efficiency Video Coding (HEVC). The proposed approach implements these 2-D transforms by two successive 1-D transforms using a well-known row-column and Even-Odd decomposition techniques. Altogether, the proposed architecture is composed of a 4-point DCT/DST unit for the smallest transform blocks (TBs), an 8/16/32-point DCT unit for the other TBs, and a transpose memory for intermediate results. On Arria II FPGA, the low-cost variant of the proposed architecture is able to support encoding of 1080p format at 60 fps and at the cost of 10.0 kALUTs and 216 DSP blocks. The respective figures for the proposed high-speed variant are 2160p at 30 fps with 13.9 kALUTs and 344 DSP blocks. These cost-performance characteristics outperform respective non-HLS approaches on FPGA.

10 citations

Proceedings ArticleDOI
16 Nov 2004
TL;DR: A novel upper bound on mean packet delay is derived under the probabilistic traffic model of Poisson arrival and any general packet length distribution and it is much simpler and tighter than the known ones by M Hawa et al., (2002).
Abstract: Fair queueing (FQ) algorithms which aim at approximating the generalized processor sharing (GPS) policy remain most popular for the provision of quality-of-service guarantees in IP networks. In this paper, we extend the notion of feasible partition introduced by Zhang et al., for the analysis of idealized GPS discipline and apply it to the stochastic bound analysis of GPS-based packetized FQ algorithms. A novel upper bound on mean packet delay is derived under the probabilistic traffic model of Poisson arrival and any general packet length distribution and it is much simpler and tighter than the known ones by M. Hawa et al., (2002). Moreover, the derived upper bound fits a class of GPS-based packetized FQ algorithms including WFQ, SCFQ and SPFQ.

10 citations

Proceedings ArticleDOI
16 Nov 2004
TL;DR: An adaptive resource allocation model that is based on the WRR queuing discipline is presented and it is shown that the total revenue can be increased due to the allocation of unused resources to more expensive service classes.
Abstract: This paper presents an adaptive resource allocation model that is based on the WRR queuing discipline. The model ensures QoS requirements and tries to maximize a service provider's revenue by manipulating weights of the WRR scheduler. To adjust the weights, it is proposed to use the revenue criterion, which controls the allocation of free resources. The simulation considers a single node with the implemented model that serves several classes with different QoS requirements and traffic characteristics. It is shown that the total revenue can be increased due to the allocation of unused resources to more expensive service classes. Furthermore, the adaptive model eliminates the need to find the optimal static weight values as they are calculated dynamically.

10 citations

Proceedings ArticleDOI
16 Apr 2007
TL;DR: A Whirlpool hashing hardware core suited for devices in which low cost is desired, and constitutes of a novel 8-bit architecture that allows compact realizations of the algorithm.
Abstract: Weaknesses have recently been found in the widely used cryptographic hash functions SHA-1 and MD5. A potential alternative for these algorithms is the Whirlpool hash function, which has been standardized by ISO/IEC and evaluated in the European research project NESSIE. In this paper we present a Whirlpool hashing hardware core suited for devices in which low cost is desired. The core constitutes of a novel 8-bit architecture that allows compact realizations of the algorithm. In the Xilinx Virtex-II Pro XC2VP40 FPGA, our implementation consumes 376 slices and achieves the throughput of 81.5 Mbit/s. The resource utilization of our design is one fourth of the smallest Whirlpool implementation presented to date.

10 citations

Journal ArticleDOI
TL;DR: The design, implementation, and experiments of a Program Image Dissemination Protocol PIDP for autonomous WSNs is presented, which is reliable, lightweight and it supports multi-hopping.
Abstract: Resource constrained Wireless Sensor Networks WSNs require an automated firmware updating protocol for adding new features or error fixes. Reprogramming nodes manually is often impractical or even impossible. Current update protocols require a large external memory or external WSN transport protocol. This paper presents the design, implementation, and experiments of a Program Image Dissemination Protocol PIDP for autonomous WSNs. It is reliable, lightweight and it supports multi-hopping. PIDP does not require external memory, is independent of the WSN implementation, transfers firmware, and reprograms the whole program image. It was implemented on a node platform with an 8-bit microcontroller and a 2.4 GHz radio. Implementation requires 22 bytes of data memory and less than 7 kilobytes of program memory. PIDP updates 178 nodes within 5 hours. One update consumes under 1‰ of the energy of two AA batteries.

9 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