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Uncontrollable manufacturing variations in electrical hardware circuits can be exploited as Physical Unclonable Functions (PUFs). Herein, we present a Printed Electronics (PE)-based PUF system architecture. Our proposed Differential Circuit PUF (DiffC-PUF) is a hybrid system, combining silicon-based and PE-based electronic circuits. The novel approach of the DiffC-PUF architecture is to provide a specially designed real hardware system architecture, that enables the automatic readout of interchangeable printed DiffC-PUF core circuits. The silicon-based addressing and evaluation circuit supplies and controls the printed PUF core and ensures seamless integration into silicon-based smart systems. Major objectives of our work are interconnected applications for the Internet of Things (IoT).
Vehicle-to-Everything (V2X) communication promises improvements in road safety and efficiency by enabling low-latency and reliable communication services for vehicles. Besides using Mobile Broadband (MBB), there is a need to develop Ultra Reliable Low Latency Communications (URLLC) applications with cellular networks especially when safety-related driving applications are concerned. Future cellular networks are expected to support novel latencysensitive use cases. Many applications of V2X communication, like collaborative autonomous driving requires very low latency and high reliability in order to support real-time communication between vehicles and other network elements. In this paper, we classify V2X use-cases and their requirements in order to identify cellular network technologies able to support them. The bottleneck problem of the medium access in 4G Long Term Evolution(LTE) networks is random access procedure. It is evaluated through simulations to further detail the future limitations and requirements. Limitations and improvement possibilities for next generation of cellular networks are finally detailed. Moreover, the results presented in this paper provide the limits of different parameter sets with regard to the requirements of V2X-based applications. In doing this, a starting point to migrate to Narrowband IoT (NB-IoT) or 5G - solutions is given.
The next generation cellular networks are expected to improve reliability, energy efficiency, data rate, capacity and latency. Originally, Machine Type Communication (MTC) was designed for low-bandwidth high-latency applications such as, environmental sensing, smart dustbin, etc., but there is additional demand around applications with low latency requirements, like industrial automation, driver-less cars, and so on. Improvements are required in 4G Long Term Evolution (LTE) networks towards the development of next generation cellular networks for providing very low latency and high reliability. To this end, we present an in-depth analysis of parameters that contribute to the latency in 4G networks along with a description of latency reduction techniques. We implement and validate these latency reduction techniques in the open-source network simulator (NS3) for narrowband user equipment category Cat-Ml (LTE-M) to analyze the improvements. The results presented are a step towards enabling narrowband Ultra Reliable Low Latency Communication (URLLC) networks.
The excessive control signaling in Long Term Evolution networks required for dynamic scheduling impedes the deployment of ultra-reliable low latency applications. Semi-persistent scheduling was originally designed for constant bit-rate voice applications, however, very low control overhead makes it a potential latency reduction technique in Long Term Evolution. In this paper, we investigate resource scheduling in narrowband fourth generation Long Term Evolution networks through Network Simulator (NS3) simulations. The current release of NS3 does not include a semi-persistent scheduler for Long Term Evolution module. Therefore, we developed the semi-persistent scheduling feature in NS3 to evaluate and compare the performance in terms of uplink latency. We evaluate dynamic scheduling and semi-persistent scheduling in order to analyze the impact of resource scheduling methods on up-link latency.
The Datagram Transport Layer Security (DTLS) protocol has been designed to provide end-to-end security over unreliable communication links. Where its connection establishment is concerned, DTLS copes with potential loss of protocol messages by implementing its own loss detection and retransmission scheme. However, the default scheme turns out to be suboptimal for links with high transmission error rates and low data rates, such as wireless links in electromagnetically harsh industrial environments. Therefore, in this paper, as a first step we provide an analysis of the standard DTLS handshake's performance under such adverse transmission conditions. Our studies are based on simulations that model message loss as the result of bit transmission errors. We consider several handshake variants, including endpoint authentication via pre-shared keys or certificates. As a second step, we propose and evaluate modifications to the way message loss is dealt with during the handshake, making DTLS deployable in situations which are prohibitive for default DTLS.
The Transport Layer Security (TLS) protocol is a cornerstone of secure network communication, not only for online banking, e-commerce, and social media, but also for industrial communication and cyber-physical systems. Unfortunately, implementing TLS correctly is very challenging, as becomes evident by considering the high frequency of bugfixes filed for many TLS implementations. Given the high significance of TLS, advancing the quality of implementations is a sustained pursuit. We strive to support these efforts by presenting a novel, response-distribution guided fuzzing algorithm for differential testing of black-box TLS implementations. Our algorithm generates highly diverse and mostly-valid TLS stimulation messages, which evoke more behavioral discrepancies in TLS server implementations than other algorithms. We evaluate our algorithm using 37 different TLS implementations and discuss―by means of a case study―how the resulting data allows to assess and improve not only implementations of TLS but also to identify underspecified corner cases. We introduce suspiciousness as a per-implementation metric of anomalous implementation behavior and find that more recent or bug-fixed implementations tend to have a lower suspiciousness score. Our contribution is complementary to existing tools and approaches in the area, and can help reveal implementation flaws and avoid regression. While being presented for TLS, we expect our algorithm's guidance scheme to be applicable and useful also in other contexts. Source code and data is made available for fellow researchers in order to stimulate discussions and invite others to benefit from and advance our work.
The paper describes the methodology and experimental results for revealing similarities in thermal dependencies of biases of accelerometers and gyroscopes from 250 inertial MEMS chips (MPU-9250). Temperature profiles were measured on an experimental setup with a Peltier element for temperature control. Classification of temperature curves was carried out with machine learning approach.
A perfect sensor should not have thermal dependency at all. Thus, only sensors inside the clusters with smaller dependency (smaller total temperature slopes) might be pre-selected for production of high accuracy inertial navigation modules. It was found that no unified thermal profile (“family” curve) exists for all sensors in a production batch. However, obviously, sensors might be grouped according to their parameters. Therefore, the temperature compensation profiles might be regressed for each group. 12 slope coefficients on 5 degrees temperature intervals from 0°C to +60°C were used as the features for the k-means++ clustering algorithm.
The minimum number of clusters for all sensors to be well separated from each other by bias thermal profiles in our case is 6. It was found by applying the elbow method. For each cluster a regression curve can be obtained.
Recently, the demand for scalable, efficient and accurate Indoor Positioning Systems (IPS) has seen a rising trend due to their utility in providing Location Based Services (LBS). Visible Light Communication (VLC) based IPS designs, VLC-IPS, leverage Light Emitting Diodes (LEDs) in indoor environments for localization. Among VLC-based designs, Time Difference of Arrival (TDOA) based techniques are shown to provide very low errors in the relative position of receivers. Our considered system consists of five LEDs that act as transmitters and a single receiver (photodiode or image sensor in smart phone) whose position coordinates in an indoor environment are to be determined. As a performance criterion, Cramer Rao Lower Bound (CRLB) is derived for range estimations and the impact of various factors, such as, LED transmission frequency, position of reference LED light, and the number of LED lights, on localization accuracy has been studied. Simulation results show that depending on the optimal values of these factors, location estimation on the order of few centimeters can be realistically achieved.
Real-Time Ethernet has become the major communication technology for modern automation and industrial control systems. On the one hand, this trend increases the need for an automation-friendly security solution, as such networks can no longer be considered sufficiently isolated. On the other hand, it shows that, despite diverging requirements, the domain of Operational Technology (OT) can derive advantage from high-volume technology of the Information Technology (IT) domain. Based on these two sides of the same coin, we study the challenges and prospects of approaches to communication security in real-time Ethernet automation systems. In order to capitalize the expertise aggregated in decades of research and development, we put a special focus on the reuse of well-established security technology from the IT domain. We argue that enhancing such technology to become automation-friendly is likely to result in more robust and secure designs than greenfield designs. Because of its widespread deployment and the (to this date) nonexistence of a consistent security architecture, we use PROFINET as a showcase of our considerations. Security requirements for this technology are defined and different well-known solutions are examined according their suitability for PROFINET. Based on these findings, we elaborate the necessary adaptions for the deployment on PROFINET.
Solar irradiance prediction is vital for the power management and the cost reduction when integrating solar energy. The study is towards a ground image based solar irradiance prediction which is highly dependent on the cloud coverage. The sky images are collected by using ground based sky imager (fisheye lens). In this work, different algorithms for cloud detection being a preparation step for their segmentation are compared.
The fisheye camera has been widely studied in the field of ground based sky imagery and robot vision since it can capture a wide view of the scene at one time. However, serious image distortion is a major drawback hindering its wider use. To remedy this, this paperproposes a lens calibration and distortion correction method for detecting clouds and forecasting solar radiation. Finally, the radial distortion of the fisheye image can be corrected by incorporating the estimated calibration parameters. Experimental results validate the effectiveness of the proposed method.
This paper deals with the detection and segmentation of clouds on high-dynamic-range (HDR) images of the sky as well as the calculation of the position of the sun at any time of the year. In order to predict the movement of clouds and the radiation of the sun for a short period of time, the clouds thickness and position have to be known as precisely as possible. Consequently, the segmentation algorithm has to provide satisfactory results regardless of different weather, illumination and climatic conditions. The principle of the segmentation is based on the classification of each pixel as a cloud or as a sky. This classification is usually based on threshold methods, since these are relatively fast to implement and show a low computational burden. In order to predict if and when the sun will be covered by clouds, the position of the sun on the images has to be determined. For this purpose, the zenith and azimuth angles of the sun are determined and converted into XY coordinates.
Printed Electronics (PE) is a promising technology that provides mechanical flexibility and low-cost fabrication. These features make PE the key enabler for emerging applications, such as smart sensors, wearables, and Internet of Things (IoTs). Since these applications need secure communication and/or authentication, it is vital to utilize security primitives for cryptographic key and identification. Physical Unclonable Functions (PUF) have been adopted widely to provide the secure keys. In this work, we present a weak PUF based on Electrolyte-gated FETs using inorganic inkjet printed electronics. A comprehensive analysis framework including Monte Carlo simulations based on real device measurements is developed to evaluate the proposed PE-PUF. Moreover, a multi-bit PE-PUF design is proposed to optimize area usage. The analysis results show that the PE-PUF has ideal uniqueness, good reliability, and can operates at low voltage which is critical for low-power PE applications. In addition, the proposed multi-bit PE-PUF reduces the area usage around 30%.
Printed electronics offers certain technological advantages over its silicon based counterparts, such as mechanical flexibility, low process temperatures, maskless and additive manufacturing process, leading to extremely low cost manufacturing. However, to be exploited in applications such as smart sensors, Internet of Things and wearables, it is essential that the printed devices operate at low supply voltages. Electrolyte gated field effect transistors (EGFETs) using solution-processed inorganic materials which are fully printed using inkjet printers at low temperatures are very promising candidates to provide such solutions. In this paper, we discuss the technology, process, modeling, fabrication, and design aspect of circuits based on EGFETs. We show how the measurements performed in the lab can accurately be modeled in order to be integrated in the design automation tool flow in the form of a Process Design Kit (PDK). We also review some of the remaining challenges in this technology and discuss our future directions to address them.
In public transportation, the motor pool often consists of various different vehicles bought over a duration of many years. Sometimes, they even differ within one batch bought at the same time. This poses a considerable challenge in the storage and allocation of spare parts, especially in the event of damage to a vehicle. Correctly assigning these parts before the vehicle reaches the workshop could significantly reduce both the downtime and, therefore, the actual costs for companies. In order to achieve this, the current software uses a simple probability calculation. To improve the performance, the data of specific companies was analysed, preprocessed and used with several modelling techniques to classify and, therefore, predict the spare parts to be used in the event of a faulty vehicle. We summarize our experience running through the steps of the Cross Industry Standard Process for Data Mining and compare the performance to the previously used probability. Gradient Boosting Trees turned out to be the best modeling technique for this special case.
This paper describes the use of the single-linkage hierarchical clustering method in outlier detection for manufactured metal work pieces. The main goal of the study is to group defects that occur 5 mm into a work piece from the edge, i.e., the border of the metal work piece. The goal is to remove defects outside the area of interest as outliers. According to the assumptions made for the performance criteria, the single-linkage method has achieved better results compared to other agglomeration methods.
Targeting complex fractionated atrial electrocardiograms by automated algorithms during ablation of persistent atrial fibrillation has produced conflicting outcomes in previous electrophysiological studies and catheter ablation of atrial fibrillation and ventricular tachycardia. The aim of the investigation was to evaluate atrial and ventricular high frequency fractionated electrical signals with signal averaging technique.
Methods: Signal averaging electrocardigraphy allows high resolution ECG technique to eliminate interference noise signals in the recorded ECG. The algorithm use automatic ECG trigger function for signal averaged transthoracic, transesophageal and intra-cardiac ECG signals with novel LabVIEW software.
Results: The analysis in the time domain evaluated fractionated atrial signals at the end of the signal averaged P-wave and fractionated ventricular signals at the end of the QRS complex. We evaluated atrial flutter in the time domain with two-to-one atrioventricular conduction, 212.0 ± 4.1 ms atrial cycle length, 426.0 ± 8.2 ms ventricular cycle length, 58.2 ± 1.8 ms P-wave duration, 119.6 ± 6.4 ms PQ duration, 103.0 ± 2.4 ms QRS duration and 296.4 ± 6.8 ms QT duration. The analysis in the frequency domain evaluated high frequency fractionated atrial signals during the P-wave and high frequency fractionated ventricular signals during QRS complex.
Conclusions: Spectral analysis of signal averaging electrocardiography with novel LabVIEW software can be utilized to evaluate atrial and ventricular conduction delays in patients with atrial fibrillation and ventricular tachycardia. Complex fractionated atrial and ventricular electrocardiograms may be useful parameters to evaluate electrical cardiac bradycardia and tachycardia signals in atrial fibrillation and ventricular tachycardia ablation.
Cardiac resynchronization therapy (CRT) with hemodynamic optimized biventricular pacing is an established therapy for heart failure patients with sinus rhythm, reduced left ventricular ejection fraction and wide QRS complex. The aim of the study was to evaluate electrical right and left cardiac atrioventricular delay and left atrial delay in CRT responder and non-responder with sinus rhythm.
Methods: Heart failure patients with New York Heart Association class 3.0 ± 0.3, sinus rhythm and 27.7 ± 6.1% left ventricular ejection fraction were measured by surface ECG and transesophageal bipolar left atrial and left ventricular ECG before implantation of CRT devices. Electrical right cardiac atrioventricular delay was measured between onset of P wave and onset of QRS complex in the surface ECG, left cardiac atrioventricular delay between onset of left atrial signal and onset of left ventricular signal in the transesophageal ECG and left atrial delay between onset and offset of left atrial signal in the transesophageal ECG.
Results: Electrical atrioventricular and left atrial delay were 196.9 ± 38.7 ms right and 194.5 ± 44.9 ms left cardiac atrioventricular delay, and 47.7 ± 13.9 ms left atrial delay. There were positive correlation between right and left cardiac atrioventricular delay (r = 0.803 P < 0.001) and negative correlation between left atrial delay and left ventricular ejection fraction (r = −0.694 P = 0.026) with 67% CRT responder.
Conclusions: Transesophageal electrical left cardiac atrioventricular delay and left atrial delay may be useful preoperative atrial desynchronization parameters to improve CRT optimization.
Hintergrund: Richtung und Stärke des elektrischen Feldes (E-Feld) der biventrikulären (BV) Stimulation und elektrische interventrikuläre Desynchronisation sind bei Patienten mit Herzinsuffizienz und verbreitertem QRS Komplex von Bedeutung für den Erfolg der kardialen Resynchronisationstherapie (CRT). Das 3D Herzrhythmusmodell (HRM) ermöglicht die
Simulation von CRT und Hochfrequenz (HF) Ablation. Das Ziel der Studie besteht in der Integration von Schrittmacher- und Ablationselektroden in das HRM zur E-Feld Simulation der BV Stimulation und thermischen Feld (T-Feld) Simulation der HF Ablation von Vorhofflimmern (AF).
Methoden: Es wurden fünf multipolare linksventrikuläre (LV) Elektroden, eine epikardiale LV Elektrode, vier bipolare rechtsatriale (RA) Elektroden, zwei rechtsventrikuläre (RV) Elektroden und ein HF Ablationskatheter mit CST (Computer Simulation Technology, Darmstadt) modelliert und das HRM (Schalk et al: Clin Res Cardiol 106, Suppl 1, April 2017, P1812) um den Koronarvenensinus (CS) erweitert (HRM-CS). E-Feld Simulationen bei vorhofsynchroner BV Stimulation und bei RA Stimulation mit RV und LV Ableitung erfolgten mit den Elektroden Select Secure 3830, Capsure VDD-2 5038 und Attain OTW 4194 im HRM+CS (Fig.). F-Feld Simulationen der HF Ablation von AF bei CRT wurden mit integriertem Ablationskatheter AlCath G FullCircle (Biotronik) simuliert.
Ergebnisse: HRM-CS ermöglichte 3D E-Feld Simulationen bei vorhofsynchroner bipolarer BV Stimulation und bei bipolarer RA Stimulation mit bipolarer RV und LV Ableitung. RV und LV Stimulation erfolgten zeitgleich bei einer Amplitude von 3 V an der LV Elektrode und 1 V an der RV Elektrode mit einer Impulsbreite von jeweils 0,5 ms. Die von der BV Stimulationen erzeugten Fernpotentiale konnten von der RA Elektrode wahrgenommen werden. Das Fernpotential an der RA Elektrodenspitze betrug 32,86 mV und in 1 mm Abstand von der RA Elektrodenspitze ergab sich ein Fernpotential von 185,97 mV. HRM-CS ermöglichte 3D T-Feld Simulationen der HF Ablation von AF bei CRT. Das T-Feld bei HF Ablation des AV-Knotens wurde mit einer anliegenden Leistung von 5 W bei 420 kHz an der distalen 8 mm Ablationselektrode simuliert. Die Temperatur an der Katheterspitze betrug nach 5 s Ablationsdauer 88,66 °C, in 1 mm Abstand von der Katheterspitze im Myokard 42,17 °C und in 2 mm Abstand 37,49 °C.
Schlussfolgerungen: HRM-CS und Elektrodenmodelle ermöglichen die 3D Simulationen von E-Feldern bei vorhofsynchroner BV Stimulation, RA Stimulation mit RV und LV Wahrnehmung und von T-Feldern bei HF Ablation. E-Feld Simulationen von RA, RV und LV Stimulation und Sensing können möglicherweise zur Vorhersage von CRT Respondern genutzt werden.