Flow table lookup is a well-known bottleneck in software-defined network switches. Associative lookup is the fastest but most costly method. On the other hand, an approximate flow classification based on Bloom filters has an outstanding cost-benefit ratio but comes with a downside of false-positive results. Therefore, we propose a new flow table lookup scheme based on Bloom filters and RAM, which offers a good compromise between cost and performance. We solve the problem of false positives of primary Bloom filters by verifying the results and, if necessary, by linearly searching the contents of secondary RAM. Also, we provide a practical implementation in the FPGA-based SDN switch and experimentally show that the proposed solution can achieve better performance than the classic linear search at the low cost typical of Bloom filters.

In this paper, the error performance of coherent systems in presence of imperfect carrier phase estimation is investigated for signals propagating over the two-ray with diffuse power (TWDP) fading channels, in case when synchronization is performed using pilot carrier located out of the signal’s band-width. In that sense, closed-form approximate average binary error probability (ABEP) expressions are derived for binary and quadrature phase shift keying (BPSK and QPSK) modulated signals, with the carrier extracted using phase-locked loop (PLL) and phase noise approximated by Tikhonov probability density function (PDF). Derived expressions are calculated for various combinations of channel and phase loop parameters, enabling us to observe their effects on overall system performance. The accu-racy of derived expressions is verified through their comparison with the exact ABEPs obtained by numerical integration of the appropriate expressions.

This paper addresses the use of deep learning techniques in 3D point cloud labeling of environment representations for the task of a semantic visual localization of mobile robots. In contrast to standard problems resolved with Convolutional Neural Networks (CNNs), the paper deals with applying CNNs to segment point clouds that are, unlike images, unordered and unstructured. The used point clouds contain laser measurements of 3D positions (x,y,z) as well as captured RGB camera images from the scanned scene to colorize the point cloud (RGB values). The main focus of the paper is on implementation and evaluation of a hand-crafted convolution layer and the ConvPoint CNN architecture that introduces continuous convolutions for point cloud processing. The solution was implemented in the Python programming language using the PyTorch deep learning framework.

Instantaneous frequency measurement is a critical component of power system control and automation. Recently, electric power distribution networks with a high proportion of renewable energy have been subjected to unprecedented complexity, necessitating more complicated automation solutions. The major reasons for frequency changes include the usage of dispersed generation, the connection of non-linear loads, and the occurrence of some unforeseen system problems. This paper presents two DFT-based power system frequency measuring algorithms. It considers frequency variations from the system’s fundamental frequency, as well as the noise generated by analog to digital converters (ADC). The IEEE Phasor Measurement Unit (PMU) latest Standard specification (IEC/IEEE 60255-118-1:2018) is used to examine these two methodologies. The methodologies are evaluated using test signals that are required to provide PMU quality evaluation and classification while accounting for process noise, ADC conversion noise, and dynamically changing input voltage and current signals. The tradeoff between DFT simplicity in implementation and needed complexity of power systems is put to the test by abrupt variations in frequency and amplitude of the fundamental component.

Ambient conditions, especially temperature and humidity, have a huge impact on the performance of an air quality sensor. In this paper, four correction models were built to compensate the impact of ambient conditions. Linear regression and machine learning algorithms were used for building the models. Correction models were trained by using three types of measurement data. Raw measurement data was used in the first case. Secondly, measurement data was corrected and a significant improvement was shown. Lastly, measurements of various ambient conditions were used as well. Using corrected and extended measurement data brought a great improvement in accuracy of the models. A neural network correction model proved to be the most efficient in all cases. Compensating the impact of ambient conditions on the performance of an air quality sensor by using correction models was efficient and this method could be used in the air quality monitoring applications. This is of particular importance for usage of low-cost sensors in the air quality monitoring.

The broader use of devices powered by rechargeable batteries, especially constrained embedded devices, makes the efficient Battery Management System (BMS) increasingly more important. The estimation accuracy of the amount of remaining charge in the battery is critical as it affects the device’s operation and reliability. For that reason, the estimation of state-of-charge (SoC) is considered one of the main functionalities of a BMS. However, SoC estimation remains a complex task that depends on a range of internal and external factors. Most traditional SoC estimation methods are either computationally complex, require special laboratory equipment or additional configuration efforts. In addition, most methods require continuous measurement of battery parameters, which, in turn, renders these methods not applicable to the class of constrained embedded devices. This paper aims to extend the Coulomb counting method to the class of duty-cycled energy-constrained devices by designing an algorithm that combines voltage-based evaluation and pre-recorded task power profiles to estimate the SoC. In addition, a setup for identifying the battery parameters and algorithm validation setup were also developed and described in the paper.

In this paper approach for the experimental determination of the grounding system impulse impedance under the presence of the high-frequency electromagnetic interference is presented. The considered approach is based on the application of the discrete wavelet transform on the measured signals. Validation of the considered approach has been conducted in several experiments using a vertical grounding electrode. The experimental investigation has been performed using different impulse current peak values and different front rise times. On all measured current and voltage waveforms, high-frequency interferences were registered.

This paper treats the problem of 3D outdoor environment mapping using images acquired by Unmanned Aerial Vehicle (UAV). The main focus is on the generation of 3D model for large scale environments. In order to perform 3D model reconstruction and mapping from 2D aerial images we employed a Structure from Motion (SfM) based approach. The obtained results using this approach for different scenarios, the rubble field and village, are presented. The generated UAV 3D point cloud data are compared with the ground truth using the least square method, where the ground truth represents a reference model with high accuracy geodetic precision. The comparison of the 3D environment models with the rubble field and village scenarios and the ground truth data is also given.

The Advanced Distribution Management System (ADMS) has grown to be a highly complicated system that comprises distribution generation, batteries, power electronics, and, in case of an urban area, an electric transportation system. One of the most essential features of ADMS is maintaining node voltages and branch thermal ratings within defined limits while maintaining minimal system losses and maximizing the use of renewable energy. Voltage VAr control (VVC) is extensively used to address these challenges and is becoming increasingly significant in ADMS. A side from the necessity to manage the system status, VVC must be adaptable to accommodate future Smart City (SC) requirements such as electric-vehicle charging and energy recuperation management. The majority of existing systems control the DC electric transportation system separately from the entire AC system. This paper attempts to tackle the problem using a hybrid single model that incorporates both: AC and DC network components.

The scientific discipline of Computer Vision (CV) is a fast developing branch of Machine Learning (ML). It addresses various tasks important for robotics, medicine, autonomous driving, surveillance, security or scene understanding. The development of sensor technologies enabled wide usage of 3D sensors, and therefore, it increased the interest of the CV research community in creating methods for 3D sensor data. This paper outlines seven CV tasks with 3D point cloud data, state-of-the-art techniques, and datasets. Additionally, we identify key challenges.

The article presents a series of measurements conducted on the fully-operated Quantum Key Distribution system. These measurements primarily focus on the Quantum Bit Error Rate (QBER), which is the most important parameter of the quantum channel. This parameter was observed and measured for 16 days under the quantum channel’s operating conditions to determine any correlations between the QBER and other quantum link parameters, such as secret key rate. A thorough statistical analysis of the measured data was performed as a part of this investigation and is presented in the paper.

A QKD network can be considered an add-on technology to a standard communication network that provides IT-secure cryptographic keys as a service. As a result, security challenges resulting in the suspension of functional work must be addressed. This study analyzes a Denial of Service (DoS) attack on the Key Management System (KMS), one of the critical components of the QKD network in charge of key management and key provisioning to authorized consumers. Through simulation methods performed in the QKDNetSim, we show that legitimate customers experience significantly worse service during an excessive DoS attack on KMS.

New generation networks are facing ever greater demands. When testing new network devices that must process packets at extremely high rates, it is essential to test their functionality and desired performance under maximum traffic load. As a result, in order to test the hardware, a traffic generator is required. This paper proposes an affordable and extensible high-speed FPGA-based Ethernet traffic generator. The proposed solution is able of fully utilizing a 40GbE link, with the possibility of manipulating traffic characteristics at the level of an individual packet. Although intended to run on the DE10-Pro system, the proposed design is portable to other FPGA boards with minimal development effort and changes.

The detonation properties of nonideal explosives are highly dependent on charge diameter and existence and properties of confinement. In this study, the effect of different confinements on the detonation velocity of ANFO explosives was experimentally determined along with the results of the plate dent test. ANFO explosive was selected as one of the most commonly used nonideal explosives. Following the measurement results, we found that the detonation velocity increased with increasing wall thickness, and the velocity increase was different for different confinement materials. A strong correlation existed between the ratio of the mass of confiner and explosive (M/C) and the detonation velocity (R = 0.995), and between (M/C) and the depth of the dent (δ) (R = 0.975). The data presented in this paper represent preliminary findings in developing a confinement model required for reliable numerical modeling of nonideal explosives.

Various devices and monitoring systems have been developed and deployed in order to monitor the power grid. Indeed, several real-world cyberattacks on power grid systems have been publicly reported. For the transmission and distribution, Phasor Measurement Units (PMUs) constitute the main sensing equipment of the overall wide area monitoring and situational awareness systems by collecting high-resolution data and sending them to Phasor Data Concentrators (PDCs). In this paper, we consider data spoofing attacks against PMU networks. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. We consider one potential attack, where an adversary may simply keep injecting a repeated measurement through a compromised PMU to disrupt the monitoring system. This attack is referred to as a Repeated Last Value (RLV) attack. We develop and evaluate countermeasures against RLV attacks using a 2D Convolutional Neural Network (CNN)-based approach, which operates in frames for each second mimicking images, in order to avoid the computational overhead of the classical sample-based classification algorithms, such as SVM. Further, we take this frame-based approach and use it with Support Vector Machine (SVM) for performance evaluation. Our preliminary results show that frame-based CNN as well as SVM provide promising results for RLV attacks while the efficacy of CNN over SVM frame becomes more pronounced as the attack intensity increases.