Conference Agenda

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Session Overview
Session 5: Student Session
Friday, 26/Apr/2019:
11:00am - 12:30pm

Session Chair: Ondrej Novak, TU Liberec
Location: Doubletree by Hilton City Plaza

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Design of a True Random Number Generator Based on Low Power Oscillator with Increased Jitter

Mariusz Derlecki, Krzysztof Siwiec, Pawel Narczyk, Witold Adam Pleskacz

Warsaw University of Technology, Poland

This paper presents the design of an oscillator-based true random number generator. The operation of the presented TRNG architecture is based on sampling a high-frequency oscillator output with a clock generated by a low-frequency noisy oscillator. The recycling folded cascode architecture was used for low power noise amplifier. A new method to achieve higher jitter in the low frequency oscillator is presented. The bit rate of the designed TRNG is 1.02 Mb/s. The circuit power consumption is 67 µW. The results of the simulations and statistical tests of the designed random number generator are also presented in this paper.

Analyzing and Optimizing the Dummy Rounds Scheme

Stanislav Jeřábek, Jan Schmidt

Czech Technical University in Prague, Czech Republic

The dummy rounds protection scheme, intended to offer resistance against Side Channel Attacks to Feistel and SP ciphers, has been introduced in earlier work. Its experimental evaluation revealed weaknesses, most notably in the first and last round. In this contribution, we show that the situation can be greatly improved by controlling the transition probabilities in the state space of the algorithm.

We derived necessary and sufficient conditions for the round execution probabilities to be uniform and hence the minimum possible. The optimum trajectories over the state space are regular and easy to implement.

Automated Integration of Dynamic Power Management into FPGA-Based Design

Michal Škuta, Dominik Macko

Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Slovak Republic

A low power or energy efficient hardware operation is nowadays gaining attention. It is especially true for battery-operated or energy-harvesting devices, such as most of the Internet of Things end nodes. For specific applications with rather limited market, the FPGAs are very good alternative. However, evolution of these devices is focused on high-level programming, giving application designers space to focus on application function rather than to be concerned about its low-level implementation on FPGA device - it is handled by automation tools. Thus, new FPGA-application designers are nowadays not very familiar with hardware aspects and it is difficult for them to apply power-reduction techniques in order to create an energy-efficient system. This paper is focused on automation of power-management integration into the FPGA-application design based on abstract specification, which is easy-to-use even for unfamiliar designers. It simplifies and speeds-up the low-power and energy-efficient FPGA-application design process. Moreover, the automation prevents many human-errors and thus it also alleviates the verification process. Experimental results indicate that the proposed power-management scheme is working correctly and it can be automatically generated.

Development of Wearable Hardware Platform to Measure the ECG and EMG with IMU to Detect Motion Artifacts

Muhammad TANWEER, Kari Halonen

Aalto University, Finland

Weareable biomedical devices make it possible to monitor physiological parameters of human beings where physical fitness is critical for their work. However, the motion artifacts corrupt the ambulatory measurements of electrophysiological parameters and it is necessary to detect and eliminate these motion artifacts. The long term measurement and analysis of health parameters require enormous data processing and storage resources on board. It is also challenging to perform sensor fusion of multiple devices and to manage multiple communication channels. This paper describes the development of a wearable hardware platform to measure electrocardiogram (ECG) and electromyogram (EMG) with an additional IMU sensor to detect the motion artifacts. Bringing all the sensors on single platform resolves the sensor fusion problems. The measurements are digitized and sent wirelessly through a bluetooth interface to a remote unit in real-time. Where extensive processing and analysis algorithms are applied to detect motion artifacts and extract The features of the ECG and EMG waveform structures.

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