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This work presents a new concept to implement the elliptic curve point multiplication (PM). This computation is based on a new modular arithmetic over Gaussian integer fields. Gaussian integers are a subset of the complex numbers such that the real and imaginary parts are integers. Since Gaussian integer fields are isomorphic to prime fields, this arithmetic is suitable for many elliptic curves. Representing the key by a Gaussian integer expansion is beneficial to reduce the computational complexity and the memory requirements of secure hardware implementations, which are robust against attacks. Furthermore, an area-efficient coprocessor design is proposed with an arithmetic unit that enables Montgomery modular arithmetic over Gaussian integers. The proposed architecture and the new arithmetic provide high flexibility, i.e., binary and non-binary key expansions as well as protected and unprotected PM calculations are supported. The proposed coprocessor is a competitive solution for a compact ECC processor suitable for applications in small embedded systems.
Shared Field, Divided Field
(2020)
The IETF, concerned with the evolution of the Internet architecture, nowadays also looks into industrial automation processes. The contributions of a variety of IETF activities, initiated during the last ten years, enable now the replacement of proprietary standards by an open standardized protocol stack. This stack, denoted in the following as 6TiSCH-stack, is tailored for industrial internet of things (IIoTs). The suitability of 6TiSCH-stack for Industry 4.0 is yet to explore. In this paper, we identify four challenges that, in our opinion, may delay or hinder its adoption. As a prime example of that, we focus on the initial 6TiSCHnetwork
formation, highlighting the shortcomings of the default procedure and introducing our current work for a fast and reliable formation of dense network.
The recovery of our body and brain from fatigue directly depends on the quality of sleep, which can be determined from the results of a sleep study. The classification of sleep stages is the first step of this study and includes the measurement of vital data and their further processing. The non-invasive sleep analysis system is based on a hardware sensor network of 24 pressure sensors providing sleep phase detection. The pressure sensors are connected to an energy-efficient microcontroller via a system-wide bus. A significant difference between this system and other approaches is the innovative way in which the sensors are placed under the mattress. This feature facilitates the continuous use of the system without any noticeable influence on the sleeping person. The system was tested by conducting experiments that recorded the sleep of various healthy young people. Results indicate the potential to capture respiratory rate and body movement.
In my research sabbatical I was working on three different topics, namely orthogonal polynomials in geometric modeling, re-parametrized univariate subdivision curves, and reconstruction of 3d-fish-models and other zoological artifacts. In the subsequent Sections, I will describe my particular activity in these different fields. The sections are meant to present an overview of my research activities, leaving out the technical details.
Section 1 is on orthogonal polynomials and other related generating systems for functions systems of smooth function.
In Section 2, I will discuss the application of various re-parametrization schemes for interpolatory subdivision algorithms for the generation of space curves.
The next Section 3 is concerned with my research at the University of Queensland, Brisbane, in collaboration with Dr. Ulrike Siebeck from the School of Biomedical Sciences on fish behavior and reconstruction of 3d-fish models in particular.
In the last Section 4, I will describe what effects this research will have on in my subsequent teaching at the University of Applied Science Konstanz (HTWG).
Polysomnography is a gold standard for a sleep study, and it provides very accurate results, but its cost (both personnel and material) are quite high. Therefore, the development of a low-cost system for overnight breathing and heartbeat monitoring, which provides more comfort while recording the data, is a well-motivated challenge. The system proposed in this manuscript is based on the usage of resistive pressure sensors installed under the mattress. These sensors can measure slight pressure changes provoked during breathing and heartbeat. The captured signal requires advanced processing, like applying filters and amplifiers before the analog signal is ready for the next step. Then, the output signal is digitalized and further processed by an algorithm that performs a custom filtering before it can recognize breathing and heart rate in real-time. The result can be directly visualized. Furthermore, a CSV file is created containing the raw data, timestamps, and unique IDs to facilitate further processing. The achieved results are promising, and the average deviation from a reference device is about 4bpm.
Good sleep is crucial for a healthy life of every person. Unfortunately, its quality often decreases with aging. A common approach to measuring the sleep characteristics is based on interviews with the subjects or letting them fill in a daily questionnaire and afterward evaluating the obtained data. However, this method has time and personal costs for the interviewer and evaluator of responses. Therefore, it would be important to execute the collection and evaluation of sleep characteristics automatically. To do that, it is necessary to investigate the level of agreement between measurements performed in a traditional way using questionnaires and measurements obtained using electronic monitoring devices. The study presented in this manuscript performs this investigation, comparing such sleep characteristics as "time going to bed", "total time in bed", "total sleep time" and "sleep efficiency". A total number of 106 night records of elderly persons (aged 65+) were analyzed. The results achieved so far reveal the fact that the degree of agreement between the two measurement methods varies substantially for different characteristics, from 31 minutes of mean difference for "time going to bed" to 77 minutes for "total sleep time". For this reason, a direct exchange of objective and subjective measuring methods is currently not possible.
The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two new recent algorithms (particle Bernstein and a Monte Carlo-based regression) both in terms of accuracy and processing time. A data preprocessing phase was also considered to improve the performance of the machine learning procedures, in order to reduce the problem size and a detailed analysis of the compression strategy and results is also presented.
The actual task of electrocardiographic examinations is to increase the reliability of diagnosing the condition of the heart. Within the framework of this task, an important direction is the solution of the inverse problem of electrocardiography, based on the processing of electrocardiographic signals of multichannel cardio leads at known electrode coordinates in these leads (Titomir et al. Noninvasiv electrocardiotopography, 2003), (Macfarlane et al. Comprehensive Electrocardiology, 2nd ed. (Chapter 9), 2011).
This article introduces the Global Sanctions Data Base (GSDB), a new dataset of economic sanctions that covers all bilateral, multilateral, and plurilateral sanctions in the world during the 1950–2016 period across three dimensions: type, political objective, and extent of success. The GSDB features by far the most cases amongst data bases that focus on effective sanctions (i.e., excluding threats) and is particularly useful for analysis of bilateral international transactional data (such as trade flows). We highlight five important stylized facts: (i) sanctions are increasingly used over time; (ii) European countries are the most frequent users and African countries the most frequent targets; (iii) sanctions are becoming more diverse, with the share of trade sanctions falling and that of financial or travel sanctions rising; (iv) the main objectives of sanctions are increasingly related to democracy or human rights; (v) the success rate of sanctions has gone up until 1995 and fallen since then. Using state-of-the-art gravity modeling, we highlight the usefulness of the GSDB in the realm of international trade. Trade sanctions have a negative but heterogeneous effect on trade, which is most pronounced for complete bilateral sanctions, followed by complete export sanctions.