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RETIS – Real-Time Sensitive Wireless Communication Solution for Industrial Control Applications
(2020)
Ultra-Reliable Low Latency Communications (URLLC) has been always a vital component of many industrial applications. The paper proposes a new wireless URLLC solution called RETIS, which is suitable for factory automation and fast process control applications, where low latency, low jitter, and high data exchange rates are mandatory. In the paper, we describe the communication protocol as well as the hardware structure of the network nodes for implementing the required functionality. Many techniques enabling fast, reliable wireless transmissions are used – short Transmission Time Interval (TTI), Time-Division Multiple Access (TDMA), MIMO, optional duplicated data transfer, Forward Error Correction (FEC), ACK mechanism. Preliminary tests show that reliable end-to-end latency down to 350 μs and packet exchange rate up to 4 kHz can be reached (using quadruple MIMO and standard IEEE 802.15.4 PHY at 250 kbit/s).
Novel manufacturing technologies, such as printed electronics, may enable future applications for the Internet of Everything like large-area sensor devices, disposable security, and identification tags. Printed physically unclonable functions (PUFs) are promising candidates to be embedded as hardware security keys into lightweight identification devices. We investigate hybrid PUFs based on a printed PUF core. The statistics on the intra- and inter-hamming distance distributions indicate a performance suitable for identification purposes. Our evaluations are based on statistical simulations of the PUF core circuit and the thereof generated challenge-response pairs. The analysis shows that hardware-intrinsic security features can be realized with printed lightweight devices.
Modern society is more than ever striving for digital connectivity -- everywhere and at any time, giving rise to megatrends such as the Internet of Things (IoT). Already today, 'things' communicate and interact autonomously with each other and are managed in networks. In the future, people, data, and things will be interlinked, which is also referred to as the Internet of Everything (IoE). Billions of devices will be ubiquitously present in our everyday environment and are being connected over the Internet.
As an emerging technology, printed electronics (PE) is a key enabler for the IoE offering novel device types with free form factors, new materials, and a wide range of substrates that can be flexible, transparent, as well as biodegradable. Furthermore, PE enables new degrees of freedom in circuit customizability, cost-efficiency as well as large-area fabrication at the point of use.
These unique features of PE complement conventional silicon-based technologies. Additive manufacturing processes enable the realization of many envisioned applications such as smart objects, flexible displays, wearables in health care, green electronics, to name but a few.
From the perspective of the IoE, interconnecting billions of heterogeneous devices and systems is one of the major challenges to be solved. Complex high-performance devices interact with highly specialized lightweight electronic devices, such as e.g. smartphones and smart sensors. Data is often measured, stored, and shared continuously with neighboring devices or in the cloud. Thereby, the abundance of data being collected and processed raises privacy and security concerns.
Conventional cryptographic operations are typically based on deterministic algorithms requiring high circuit and system complexity, which makes them unsuitable for lightweight devices.
Many applications do exist, where strong cryptographic operations are not required, such as e.g. in device identification and authentication. Thereby, the security level mainly depends on the quality of the entropy source and the trustworthiness of the derived keys. Statistical properties such as the uniqueness of the keys are of great importance to precisely distinguish between single entities.
In the past decades, hardware-intrinsic security, particularly physically unclonable functions (PUFs), gained a lot of attraction to provide security features for IoT devices. PUFs use their inherent variations to derive device-specific unique identifiers, comparable to fingerprints in biometry.
The potentials of this technology include the use of a true source of randomness, on demand key derivation, as well as inherent key storage.
Combining these potentials with the unique features of PE technology opens up new opportunities to bring security to lightweight electronic devices and systems. Although PE is still far from being matured and from being as reliable as silicon technology, in this thesis we show that PE-based PUFs are promising candidates to provide key derivation suitable for device identification in the IoE.
Thereby, this thesis is primarily concerned with the development, investigation, and assessment of PE-based PUFs to provide security functionalities to resource constrained printed devices and systems.
As a first contribution of this thesis, we introduce the scalable PE-based Differential Circuit PUF (DiffC-PUF) design to provide secure keys to be used in security applications for resource constrained printed devices. The DiffC-PUF is designed as a hybrid system architecture incorporating silicon-based and inkjet-printed components. We develop an embedded PUF platform to enable large-scale characterization of silicon and printed PUF cores.
In the second contribution of this thesis, we fabricate silicon PUF cores based on discrete components and perform statistical tests under realistic operating conditions. A comprehensive experimental analysis on the PUF security metrics is carried out. The results show that the silicon-based DiffC-PUF exhibits nearly ideal values for the uniqueness and reliability metrics. Furthermore, the identification capabilities of the DiffC-PUF are investigated and it is shown that additional post-processing can further improve the quality of the identification system.
In the third contribution of this thesis, we firstly introduce an evaluation workflow to simulate PE-based DiffC-PUFs, also called hybrid PUFs. Hereof, we introduce a Python-based simulation environment to investigate the characteristics and variations of printed PUF cores based on Monte Carlo (MC) simulations. The simulation results show, that the security metrics to be expected from the fabricated devices are close to ideal at the best operating point.
Secondly, we employ fabricated printed PUF cores for statistical tests under varying operating conditions including variations in ambient temperature, relative humidity, and supply voltage. The evaluations of the uniqueness, bit aliasing, and uniformity metrics are in good agreement with the simulation results. The experimentally determined mean reliability value is relatively low, which can be explained by the missing passivation and encapsulation of the printed transistors. The investigation of the identification capabilities based on the raw PUF responses shows that the pure hybrid PUF is not suitable for cryptographic applications, but qualifies for device identification tasks.
The final contribution is to switch to the perspective of an attacker. To judge on the security capabilities of the hybrid PUF, a comprehensive security analysis in the manner of a cryptanalysis is performed. The analysis of the entropy of the hybrid PUF shows that its vulnerability against model-based attacks mainly depends on the selected challenge building method. Furthermore, an attack methodology is introduced to assess the performances of different mathematical cloning attacks on the basis of eavesdropped challenge-response pairs (CRPs). To clone the hybrid PUF, a sorting algorithm is introduced and compared with commonly used supervised machine learning (ML) classifiers including logistic regression (LR), random forest (RF), as well as multi-layer perceptron (MLP).
The results show that the hybrid PUF is vulnerable against model-based attacks. The sorting algorithm benefits from shorter training times compared to the ML algorithms. If the eavesdropped CRPs are erroneous, the ML algorithms outperform the sorting algorithm.
The development of Internet of Things (IoT) embedded devices is proliferating, especially in the smart home automation system. However, the devices unfortunately are imposing overhead on the IoT network. Thus, the Internet Engineering Task Force (IETF) have introduced the IPv6 Low-Power Wireless Personal Area Network (6LoWPAN) to provide a solution to this constraint. 6LoWPAN is an Internet Protocol (IP) based communication where it allows each device to connect to the Internet directly. As a result, the power consumption is reduced. However, the limitation of data transmission frame size of the IPv6 Routing Protocol for Low-power and Lossy Network’s (RPL’s) had made it to be the running overhead, and thus consequently degrades the performance of the network in terms of Quality of Service (QoS), especially in a large network. Therefore, HRPL was developed to enhance the RPL protocol to minimize redundant retransmission that causes the routing overhead. We introduced the T-Cut Off Delay to set the limit of the delay and the H field to respond to actions taken within the T-Cut Off Delay. Thus, this paper presents the comparison performance assessment of HRPL between simulation and real-world scenarios (6LoWPAN Smart Home System (6LoSH) testbed) in validating the HRPL functionalities. Our results show that HRPL had successfully reduced the routing overhead when implemented in 6LoSH. The observed Control Traffic Overhead (CTO) packet difference between each experiment is 7.1%, and the convergence time is 9.3%. Further research is recommended to be conducted for these metrics: latency, Packet Delivery Ratio (PDR), and throughput.
Do you know that for each banana bunch the complete plant must be cut as well? Only in Brazil 440 million trees are planted annually. With an average weight of 30 kg per banana plant you can estimate about 13,5 million tons of banana residues per year. Although there exist some projects to use these residues for the production of valuable products (e.g fibers for textile and paper production) most of this organic waste material is unused and left for composting on the farmland.
The basic idea of this project is to evaluate this organic waste material for converting it to a renewable and CO2 neutral fuel. Therefore, the different parts of the banana plant (heart, leaves and pseudo stem) were analyzed regarding their biogas potential (specific biogas yield and biogas production kinetics). In further studies the effect of mechanical and enzymatic pretreatments of the different parts of the plants was investigated. This examination could then be the basis for an energetic usage of this organic residue.
The biogas batch experiments were performed according to the german guideline VDI 4630 in 2-L-Batch reactors at 37°C. As biogas substrates, the heart, the leaves and the pseudo stem of the banana plant residue with and without enzymatic/mechanical pretreatment were used.
The different parts of the banana plants result in a specific biogas production yield in the range of 260-470 norm liters per kg organic dry mass.
To determine the influence of the mechanical pretreatment (particle size 1-15 mm) on the biogas production kinetics, the kinetic constants were defined and calculated. The reduction of the particle size leads to an improved biogas production kinetics. Therefore experiments will demonstrate, if the results from the batch experiments can be converted in the continuous fed biogas reactor. The experiments of the enzymatic pretreatment are still under investigation.
We propose in this work to solve privacy preserving set relations performed by a third party in an outsourced configuration. We argue that solving the disjointness relation based on Bloom filters is a new contribution in particular by having another layer of privacy on the sets cardinality. We propose to compose the set relations in a slightly different way by applying a keyed hash function. Besides discussing the correctness of the set relations, we analyze how this impacts the privacy of the sets content as well as providing privacy on the sets cardinality. We are in particular interested in how having bits overlapping in the Bloom filters impacts the privacy level of our approach. Finally, we present our results with real-world parameters in two concrete scenarios.
While prospect of tracking mobile devices' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users' location privacy and preventing mass surveillance.
We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco's access logfile.
By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are.
The benefit of our approach is that intermediate possible infected users can be identified and subsequently contacted by the agency. With such approach, we state that solely identities of possible infected users will be revealed and location privacy of others will be preserved. To this extent, it meets General Data Protection Regulation (GDPR)requirements in this area.
Time-of-Flight Cameras Enabling Collaborative Robots for Improved Safety in Medical Applications
(2020)
Human-robot collaboration is being used more and more in industry applications and is finding its way into medical applications. Industrial robots that are used for human-robot collaboration, cannot detect obstacles from a distance. This paper introduced the idea of using wireless technology to connect a Time-of-Flight camera to off-the-shelf industrial robots. This way, the robot can detect obstacles up to a distance of five meters. Connecting Time-of-Flight cameras to robots increases the safety in human-robot collaboration by detecting obstacles before a collision. After looking at the state of the art, the authors elaborated the different requirements for such a system. The Time-of-Flight camera from Heptagon is able to work in a range of up to five meters and can connect to the control unit of the robot via a wireless connection.
Neuromorphic computing systems have demonstrated many advantages for popular classification problems with significantly less computational resources. We present in this paper the design, fabrication and training of a programmable neuromorphic circuit, which is based on printed electrolytegated field-effect transistor (EGFET). Based on printable neuron architecture involving several resistors and one transistor, the proposed circuit can realize multiply-add and activation functions. The functionality of the circuit, i.e. the weights of the neural network, can be set during a post-fabrication step in form of printing resistors to the crossbar. Besides the fabrication of a programmable neuron, we also provide a learning algorithm, tailored to the requirements of the technology and the proposed programmable neuron design, which is verified through simulations. The proposed neuromorphic circuit operates at 5V and occupies 385mm 2 of area.
This paper gives an overview of the impact that the corona pandemic has on the export industry in Canada and analyzes the different Canadian government measures for exporters. In addition, the measures are subsequently evaluated in order to identify if the support measures can help Canadian exporters to overcome the crisis. The basis of this paper are semi-structured expert interviews with experts from the financial sector, scientific literature and studies. The results have shown that the COVID-19 pandemic has a major impact on Canada’s export economy and it’s GDP. Trade is only possible to a limited extent, as many borders are closed. The Canadian government reacted with an economic response plan to support Canadian individuals and businesses. This paper depicts and assesses the most eligible measures for export companies.
Strings
(2020)
This article presents the currently ongoing development of an audiovisual performance work with the title Strings. This work provides an improvisation setting for a violinist, two laptop performers, and two generative systems. At the core of Strings lies an approach that establishes a strong correlation among all participants by means of a shared physical principle. The physical principle is that of a vibrating string. The article discusses how this principle is used in both natural and simulated forms as main interaction layer between all performers and as natural or generative principle for creating audio and video.
Wow, You Are Terrible at This!: An Intercultural Study on Virtual Agents Giving Mixed Feedback
(2020)
While the effects of virtual agents in terms of likeability, uncanniness, etc. are well explored, it is unclear how their appearance and the feedback they give affects people's reactions. Is critical feedback from an agent embodied as a mouse or a robot taken less serious than from a human agent? In an intercultural study with 120 participants from Germany and the US, participants had to find hidden objects in a game and received feedback on their performance by virtual agents with different appearances. As some levels were designed to be unsolvable, critical feedback was unavoidable. We hypothesized that feedback would be taken more serious, the more human the agent looked. Also, we expected the subjects from the US to react more sensitively to criticism. Surprisingly, our results showed that the agents' appearance did not significantly change the participants' perception. Also, while we found highly significant differences in inspirational and motivational effects as well as in perceived task load between the two cultures, the reactions to criticism were contrary to expectations based on established cultural models. This work improves our understanding on how affective virtual agents are to be designed, both with respect to culture and to dialogue strategies.
Morphological transition of a rod-shaped phase into a string of spherical particles is commonly observed in the microstructures of alloys during solidification (Ratke and Mueller, 2006). This transition phenomenon can be explained by the classic Plateau-Rayleigh theory which was derived for fluid jets based on the surface area minimization principle. The quintessential work of Plateau-Rayleigh considers tiny perturbations (amplitude much less than the radius) to the continuous phase and for large amplitude perturbations, the breakup condition for the rod-shaped phase is still a knotty issue. Here, we present a concise thermodynamic model based on the surface area minimization principle as well as a non-linear stability analysis to generalize Plateau-Rayleigh’s criterion for finite amplitude perturbations. Our results demonstrate a breakup transition from a continuous phase via dispersed particles towards a uniform-radius cylinder, which has not been found previously, but is observed in our phase-field simulations. This new observation is attributed to a geometric constraint, which was overlooked in former studies. We anticipate that our results can provide further insights on microstructures with spherical particles and cylinder-shaped phases.
Short-term load forecasting (STLF) has been playing a key role in the electricity sector for several decades, due to the need for aligning energy generation with the demand and the financial risk connected with forecasting errors. Following the top-down approach, forecasts are calculated for aggregated load profiles, meaning the sum of singular loads from consumers belonging to a balancing group. Due to the emerging flexible loads, there is an increasing relevance for STLF of individual factories. These load profiles are typically more stochastic compared to aggregated ones, which imposes new requirements to forecasting methods and tools with a bottom-up approach. The increasing digitalization in industry with enhanced data availability as well as smart metering are enablers for improved load forecasts. There is a need for STLF tools processing live data with a high temporal resolution in the minute range. Furthermore, behin-the-meter (BTM) data from various sources like submetering and production planning data should be integrated in the models. In this case, STLF is becoming a big data problem so that machine learning (ML) methods are required. The research project “GaIN” investigates the improvement of the STLF quality of an energy utility using BTM data and innovative ML models. This paper describes the project scope, proposes a detailed definition for a benchmark and evaluates the readiness of existing STLF methods to fulfil the described requirements as a reviewing paper.
The review highlights that recent STLF investigations focus on ML methods. Especially hybrid models gain more and more importance. ML can outperform classical methods in terms of automation degree and forecasting accuracy. Nevertheless, the potential for improving forecasting accuracy by the use of ML models depends on the underlying data and the types of input variables. The described methods in the analyzed publications only partially fulfil the tool requirements for STLF on company level. There is still a need to develop suitable ML methods to integrate the expanded data base in order to improve load forecasts on company level.
Communication protocols enable information exchange between different information systems. If protocol descriptions for these systems are not available, they can be reverse-engineered for interoperability or security reasons. This master thesis describes the analysis of such a proprietary binary protocol, named the DVRIP or Dahua private protocol from Dahua Technology. The analysis contains the identification of the DVRIP protocol header format, security mechanisms and vulnerabilities inside the protocol implementation. With the revealing insights of the protocol, an increase of the overall security is achieved. This thesis builds the foundation for further targeted security analyses.
This paper explains the realization of a concept for research-oriented photonics education. Using the example of the integration of an actual PhD project, it is shown how students are familiarized with the topic of research and scientific work in the first semesters. Typical research activities are included as essential parts of the learning process. Research should be made visible and tangible for the students. The authors will present all aspects of the learning environment, their impressions and experiences with the implemented scenario, as well as first evaluation results of the students.
Amorphous In-Ga-Zn-O (IGZO) is a high-mobility semiconductor employed in modern thin-film transistors for displays and it is considered as a promising material for Schottky diode-based rectifiers. Properties of the electronic components based on IGZO strongly depend on the manufacturing parameters such as the oxygen partial pressure during IGZO sputtering and post-deposition thermal annealing. In this study, we investigate the combined effect of sputtering conditions of amorphous IGZO (In:Ga:Zn=1:1:1) and post-deposition thermal annealing on the properties of vertical thin-film Pt-IGZO-Cu Schottky diodes, and evaluated the applicability of the fabricated Schottky diodes for low-frequency half-wave rectifier circuits. The change of the oxygen content in the gas mixture from 1.64% to 6.25%, and post-deposition annealing is shown to increase the current rectification ratio from 10 5 to 10 7 at ±1 V, Schottky barrier height from 0.64 eV to 0.75 eV, and the ideality factor from 1.11 to 1.39. Half-wave rectifier circuits based on the fabricated Schottky diodes were simulated using parameters extracted from measured current-voltage and capacitance-voltage characteristics. The half-wave rectifier circuits were realized at 100 kHz and 300 kHz on as-fabricated Schottky diodes with active area of 200 μm × 200 μm, which is relevant for the near-field communication (125 kHz - 134 kHz), and provided the output voltage amplitude of 0.87 V for 2 V supply voltage. The simulation results matched with the measurement data, verifying the model accuracy for circuit level simulation.
Deafblindness, also known as dual sensory loss, is the combination of sight and hearing impairments of such extent that it becomes difficult for one sense to compensate for the other. Communication issues are a key concern for the Deafblind community. We present the design and technical implementation of the Tactile Board: a mobile Augmentative and Alternative Communication (AAC) device for individuals with deafblindness. The Tactile Board allows text and speech to be translated into vibrotactile signs that are displayed real-time to the user via a haptic wearable. Our aim is to facilitate communication for the deafblind community, creating opportunities for these individuals to initiate and engage in social interactions with other people without the direct need of an intervener.
Co-Designing Assistive Tools to Support Social Interactions by Individuals Living with Deafblindness
(2020)
Deafblindness is a dual sensory impairment that affects many aspects of life, including mobility, access to information, communication, and social interactions. Furthermore, individuals living with deafblindness are under a high risk of social isolation. Therefore, we identified opportunities for applying assistive tools to support social interactions through co-ideation activities with members of the deafblind community. This work presents our co-design approach, lessons learned and directions for designing meaningful assistive tools for dual sensory loss.
In the work at hand, we state that privacy and malleability of data are two aspects highly desired but not easy to associate. On the one hand, we are trying to shape data to make them usable and editable in an intelligible way, namely without losing their initial information. On the other hand, we are looking for effective privacy on data such that no external or non-authorized party could learn about their content. In such a way, we get overlapping requirements by pursuing different goals; it is trivial to be malleable without being secure, and vice versa. We propose four “real-world” use cases identified as scenarios where these two contradictory features are required and taking place in distinct environments. These considered backgrounds consist of firstly, cloud security auditing, then privacy of mobile network users and industry 4.0 and finally, privacy of COVID-19 tracing app users. After presenting useful background material, we propose to employ multiple approaches to design solutions to solve the use cases. We combine homomorphic encryption with searchable encryption and private information retrieval protocol to build an effective construction for the could auditing use case. As a second step, we develop an algorithm to generate the appropriate parameters to use the somewhat homomorphic encryption scheme by considering correctness, performance and security of the respective application. Finally, we propose an alternative use of Bloom filter data structure by adding an HMAC function to allow an outsourced third party to perform set relations in a private manner. By analyzing the overlapping bits occurring on Bloom filters while testing the inclusiveness or disjointness of the sets, we show how these functions maintain privacy and allow operations directly computed on the data structure. Then, we show how these constructions could be applied to the four selected use cases. Our obtained solutions have been implemented and we provide promising results that validate their efficiency and thus relevancy.