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The 5G Edge Intelligence for Vertical Experimentation (5G-DIVE) project aims at demonstrating the technical merits and business value proposition of 5G technologies in two vertical pilots, namely the Industry 4.0 (I4.0) and Autonomous Drones Scout (ADS) pilots. This paper presents an overview of the overall 5G-DIVE solution and reports the results of the initial validation campaign of the selected
Herglotz functions inevitably appear in pure mathematics, mathematical physics, and engineering with a wide range of applications. In particular, they are the pertinent functions to model passive systems, and thus appear in modeling of electromagnetic phenomena in circuits, antennas, materials, and scattering. In this chapter, we review the basic theory of Herglotz functions and its applications t
Optical wireless networks realized by means of gigabit optical wireless communication (OWC) systems are becoming, in a variety of applications, an important alternative, or a complementary solution, to their fiber-based counterparts. However, performance of the OWC systems can be considerably degraded in periods of unfavorable weather conditions, such as heavy fog, which temporarily reduce the eff
Connected Autonomous Vehicles (AV)s can transform urban transportation systems and have the potential to improve the safety and efficiency, since human errors and distractions are removed. However, these systems are vulnerable to model uncertainties, communication impairments associated with the wireless communication, and external disturbances. As a result, vehicles need to drive at low speed and
The learning with errors (LWE) problem is one of the main mathematical foundations of post-quantum cryptography. One of the main groups of algorithms for solving LWE is the Blum–Kalai–Wasserman (BKW) algorithm. This paper presents new improvements of BKW-style algorithms for solving LWE instances. We target minimum concrete complexity, and we introduce a new reduction step where we partially reduc
In this paper, we present a side-channel attack on a first-order masked implementation of IND-CCA secure Saber KEM. We show how to recover both the session key and the long-term secret key from 24 traces using a deep neural network created at the profiling stage. The proposed message recovery approach learns a higher-order model directly, without explicitly extracting random masks at each executio
Migration distance and seasonal redistribution patterns may vary between and within bird populations. A common pattern is leap-frog migration, in which northerly breeding populations migrate farther and winter south of more southerly breeding populations. Another common pattern is difference in migration distances between males and females within the same population. Evidently different population
Digital twins are taking a central role in the industry 4.0 narrative. However, they are still illusive. Many aspects of the digital-twins have yet to materialize. For example, to what degree will they be integrated into cloud and industry 4.0 systems as well as how and if they should augment their physical counterpart. Those choices are accompanied by challenging security aspects, many of which h
The steadily increasing Internet of Things (IoT) devices are vulnerable to be used as bots to launch distributed-denial-of-service (DDoS) attacks. In this paper, we present X-Pro, a distributed XDP proxy to counteract DDoS attacks. We propose a source-based defense mechanism where proxies located between the IoT devices and the victim performs flow policing on all IoT traffic from a single adminis
In this paper, we investigate the security of SNOW-V, demonstrating two guess-and-determine (GnD) attacks against the full version with complexities 2384 and 2378, respectively, and one distinguishing attack against a reduced variant with complexity 2303 . Our GnD attacks use enumeration with recursion to explore valid guessing paths, and try to truncate as many invalid guessing paths as possible
Cryptosystems based on the learning with errors (LWE) problem are assigned a security level that relates to the cost of generic algorithms for solving the LWE problem. This includes at least the so-called primal and dual lattice attacks. In this paper, we present an improvement of the dual lattice attack using an idea that can be traced back to work by Bleichenbacher. We present an improved distin
Efficient protection of huge amount of IoT produced data is key for wide scale data analytic services. The most efficient way is to use pure symmetric encryption as that allows both fast decryption at the analytic engine side as well as energy efficient encryption at the IoT side. However, symmetric encryption can only be performed if there is a way to directly map an encrypted object to the corre
Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, interpatient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic–pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled d
The relaxation systems are an important subclass of the passive systems that arise naturally in applications. We exploit the fact that they have highly structured state-space realisations to derive analytical solutions to some simple H-infinity type optimal control problems. The resulting controllers are also relaxation systems, and often sparse. This makes them ideal candidates for applications i
This paper proposes a convex approach to the Frisch-Kalman problem that identifies the linear relations among variables from noisy observations. The problem was proposed by Ragnar Frisch in 1930s, and was promoted and further developed by Rudolf Kalman later in 1980s. It is essentially a rank minimization problem with convex constraints. Regarding this problem, analytical results and heuristic met
Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings, the algorithms had better come with the same type of reliability, robustness, a
In this paper we consider adaptive distributed stabilization for uncertain multivariable linear systems with a time-varying diagonal matrix gain. We show that an unknown system matrix being an M-matrix is a sufficient condition to ensure uncertain linear systems to be stabilizable by matrix high gains, and derive a threshold condition to ensure exponential stability of uncertain linear systems sta
Fast and accurate homography estimation is essential to many computer vision applications, including scene degenerate cases and planarity detection. Such cases arise naturally in man-made environments, and failure to handle them will result in poor positioning estimates. Most modern day consumer cameras are affected by some level of radial distortion, which must be compensated for in order to get
In this paper we develop a number of generic techniques and algorithms in spectral analysis of large linear approximations for use in cryptanalysis. We apply the developed tools for cryptanalysis of ZUC-256 and give a distinguishing attack with complexity around 2236 . Although the attack is only 220 times faster than exhaustive key search, the result indicates that ZUC-256 does not provide a sour
In this paper we study resilience of TDMA-based wireless sensor networks to node failures. We investigate exploiting mutlicast routing for providing redundancy in the number of gateways used by data streams, so as to protect them against gateway failures. To do this, we develop an optimization model aiming at packet traffic throughput maximization composed of three mixed-integer programming proble
