Miracle-dz/UniFAR-FTUs huggingface dataset:
An Effective In-Vehicle CAN Bus Intrusion Detection System Using CNN Deep Learning Approach

The modern car is increasingly connected. That connection is magnified by the presence of a large number of electronic control units (ECUs). The communication between the ECUs of a modern car is assured by the Controller Area Network (CAN) bus system. Despite its importance, the CAN bus system is bereft of security mechanisms making it vulnerable to numerous security attacks. When an attacker succeeds in compromising the ECUs, they can take control and stop the engine, disable the brakes, turn the lights on/off, etc. An intrusion detection system (IDS) can be deployed as an appropriate security measure to detect the malicious network traffic in the CAN bus system. In this paper, we propose a Convolutional Neural Network (CNN)-based network attacks IDS for protecting the CAN bus system. For efficiency reasons, we generated our own datasets from three car models. Our experiment results demonstrate that our classifier is efficient for detecting the CAN bus system attacks, and it performs with a high accuracy of 99.99% and a detection rate of 0.99.
Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

With the expansion of the use of frequency response analysis (FRA) as a reliable tool for fault detection in transformers, more capabilities of this method are discovered every day. So that today the number of transformer faults that can be identified by FRA method has also increased. One of the most critical steps in fault detection with FRA is to distinguish faults and classify them in different classes. In this paper, well-known intelligent classifiers (probabilistic neural network, decision tree, support vector machine, and k-nearest neighbors) are used to classify transformer faults. For this purpose, the necessary measurements are performed on the model transformers under the healthy condition and under different fault conditions (axial displacement, radial deformation, disc space variation, short-circuits, and core deformation). Then, by dividing the frequency ranges of the measured transfer functions of the transformer, a new feature based on numerical and statistical indices for training and validation of classifiers is proposed. After completing the training process, the performance of the classifiers is evaluated and compared by applying the data obtained from real transformers.
Spreading Factor Allocation for LoRa Nodes Progressively Joining a Multi-Gateway Adaptive Network

In this paper, we investigate how to provide good transmission quality in massive deployments of LoRa networks by considering all parameters such as device heterogeneity, network topology, and deployment density. We consider the scenario with nodes progressively joining the network, i.e., new nodes joining the network are configured based on measured metrics and without modifying the configuration of nodes that already joined the network. Based on this assumption, we propose an algorithm to improve network performance by effectively allocating a spreading factor (SF) to end-devices in realistic multi-gateway deployments. The algorithm performs better than the Adaptive Data Rate (ADR) of LoRaWAN (e.g., it almost doubles the packet delivery ratio (PDR) in scenarios with 10k nodes) and enhances LoRa deployments by adapting the communication parameters of end-devices according to the network size and estimated metrics. The allocation decision is based on different metrics: link PDR, network PDR, and network distribution of SF per gateway. Nodes can easily derive the estimated metrics from gateway measurements.
Matching Theory-Based Joint Access Point Assignment and Power Allocation in Hybrid RF/VLC HetNet

This paper investigates the joint problem of access point (AP) assignment and power allocation in a three-tier hybrid radio frequency/visible light communication (VLC) heterogeneous network (HetNet). The target is to maximize the HetNet’s sum-rate under practical constraints such as APs’ power budgets and users’ quality-of-service constraints, while maintaining an acceptable level of illumination in the VLC system. This is an NP-hard problem due to the binary and continuous optimization variables. To obtain an efficient solution, the original problem is first reformulated into the well-known college admissions model. Then, a distributed and low-complexity algorithm based on matching theory and convex optimization is proposed to solve the joint problem. Simulation results highlight the robustness of the proposed algorithm and its significant gain in network sum-rate as compared to the considered benchmarks. Finally, the theoretical analysis on convergence, stability, and complexity of the proposed algorithm is provided.
Three-level control architecture for hybrid AC/DC distribution grids

The integration of DC technology into existing AC systems represents a possible response to challenges at all grid levels. As consequence, hybrid AC/DC systems emerge providing more flexibility in grid operation. To guarantee secure and efficient network operation of these systems, adequate operating points for controllable devices such as converters are required. We present a comparison of suitable hierarchical control concepts for different types of hybrid AC/DC networks with focus on medium-voltage. For this purpose, we evaluate three different OPF formulations used for regular set point updates. The OPFs are tested on an adapted 511 bus MV network based on real data. Moreover, we provide solutions to locally adapt set points and topological fall back measures in case of outages or changes of system state including an overview of different local controllers for set point attainment and retention.
Dynamic Network Slice Assignment in Software-Defined IoT Networks

Network slicing using technologies such as software-defined networking is a promising approach to address the diverse quality-of-service (QoS) requirements of Internet of Things (IoT) applications. Network slicing allows a single physical network to be transparently divided into separate, logically independent networks, with distinct service levels per slice. This allows the network to support the diverse requirements of different IoT verticals ranging from connected vehicles to smart agriculture. In this paper, we propose a dynamic network slice assignment scheme for software-defined IoT networks, where each application type is assigned to a distinct slice controller depending on its QoS requirements. We formulate the optimal mapping between network slices and slice controllers as an integer programming problem, and show that the proposed scheme is capable of significantly reducing the number of QoS violations compared to existing controller mapping schemes. Further, we present a scheduling scheme to dynamically re-compute the optimal mapping, based on optimal stopping theory, while taking into consideration the temporal variations in traffic and link latencies. Simulation results show that the proposed scheduling strategy is able to reduce the number and expected cost of re-computations, while remaining within the QoS violation tolerance limit.
A Low-Power Front-End with Compressive Sensing Circuit for Neural Signal Acquisition Designed in 180 nm CMOS Process

Multi-channel data acquisition of bio-signals is a promising technology that is being used in many fields these days. Compressed sensing (CS) is an innovative approach of signal processing that facilitates sub-Nyquist processing of bio-signals, such as an electrocardiogram (ECG) and electroencephalogram (EEG). This strategy can be used to lower the data rate to realize ultra-low-power performance, As the count of recording channels increase, data volume is increased resulting in impermissible transmitting power. This paper presents the implementation of a CMOS-based front-end design with the CS in the standard 180 nm CMOS process. A novel pseudo-random sequence generator is proposed, which consists of two different types of D flip-flops that are used for obtaining a completely random sequence. The power consumed by the bio-signal amplifier block is 2.35 μW. The SAR-ADC block that is designed to digitize the amplified signal consumes 277 μW of power and the power consumption value of the pseudo-random bit sequence generator (PRBS) is 344. 2μW. The sampling rate of PRBS block is 611.76 Kbps. We have considered collecting neural data from the 32 channels, and achieved an 8.5X compression rate. The low power consumption per channel confirms the importance of the proposed approach for multiple channel high-density neural interfaces.
Fuzzy logic based sensitivity study of nonlinear systems

A fuzzy logic based One-at-a-Time sensitivity study method of nonlinear systems is proposed in this paper. The advantage of the method is that it can be used for a wider parameter examination range. With the use of fuzzy logic a smooth evaluation of sensitivity study could be achieved and the degree of sensitivity of parameters could be easily compared. The method was tested with 2 case studies: the sensitivity study of a nonlinear Duffing-type vibration system and a vehicle simulation using an open-source physics simulator. With the simulation results it was shown that the method can be used effectively for sensitivity study of nonlinear systems in an extended examination range.
Auction-Driven Multiuser Beamforming with Deep Learning

Consider a multi-user wireless information transfer system where a multiple-antenna access point (AP) aims to sell its downlink radio access to the end users in an auction premise so that the social welfare is maximized. To this end, the AP holds auctions in which each user bids for its desired minimum signal-to-interference-plus-noise power ratio. Based on the user’s channel state information, bids, and service demands, the AP seeks the optimal set of users for which information streams are to be allocated through beamforming. We formulate the optimization problem of finding the optimal allocation rule, apply brute-force search approach to find all the feasible allocation sets using uplink-downlink duality-based algorithm (UDD), and obtain the optimal allocation rule. To circumvent the time-greediness of conventional optimization methods such as semi-definite relaxation or UDD, we propose a deep neural network architecture, and use it to solve the optimization problem with a good accuracy. The training data is collected by solving offline plenty of network realizations via the application of the UDD algorithm.
Progressive Channel Estimation and Passive Beamforming for RIS-Assisted OFDM Systems

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the energy and/or spectrum efficiency of future wireless communications. In this paper, we propose a transmission protocol for the wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system to execute channel estimation and passive beamforming simultaneously. A new channel estimation method is proposed to progressively resolve the channel state information (CSI) over the training symbols, based on which the passive beamforming at the RIS is optimized to improve the channel gains on the data tones in the remaining training symbols. Based on the incomplete CSI, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different subcarriers and different receive antennas. Moreover, we propose a low-complexity algorithm to find a high-quality solution for the formulated problem. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods.
Listening Enhancement in Noisy Environments: Solutions in Time and Frequency Domain

The intelligibility of speech from a telephone or a public address system is often affected by acoustical background noise in the near-end listening environment. Speech intelligibility and listening effort can be improved by adaptive pre-processing of the loudspeaker signal. This is called Near-End Listening Enhancement (NELE). The speech spectrum is dynamically modified, taking the acoustical background noise at the near-end into account. In this paper, two opposite NELE strategies with either Noise-Masking-Proportional Shaping or Noise-Masking-Inverse Shaping are proposed which are appropriate for different noise characteristics. Both strategies are formulated in closed form in the frequency domain. They do not require to optimize an intelligibility measure but use explicitly the masking threshold. Motivated by the frequency domain approach, a simpler time domain solution is derived which is based on linear prediction techniques and does not need the masking calculations. The proposed NELE solutions outperform state-of-the-art in terms of computational complexity, memory requirement, continuous processor load, and latency.
A Dual-Mode Modulation Technique for Controlling the Average Neutral Point Current in Neutral-Point-Clamped Converters

This article presents a dual-mode modulation technique that aims to control the average current flow into the neutral point (NP) of the NP-clamped (NPC) converter without the need for any additional hardware. The two modes of operation are normal mode and compensating mode. In the normal operation mode, all the three phases switch between two consecutive voltage levels (between the positive or negative dc-rail and the NP) in a switching period. In the COM, at least one of the phases switches between the positive and negative dc-rails in a switching period. An analytical solution is developed to determine the duration of these two operation modes within each fundamental cycle based on the converter's operating condition. An advantage of this solution is that it can be generalized for balancing the capacitor voltages in all applications employing NPC converters. The proposed solution also determines the maximum average NP current injection capacity of the NPC converter under dual-mode modulation technique, which indicates the stable operating range of the converter. The performance of the proposed modulation technique is validated experimentally for various loading conditions.
Optimal Communication-Computing-Caching for Maximizing Revenue in UAV-Aided Mobile Edge Computing

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for Internet of Things (IoT). Constrained by the limited computation and battery capabilities of IoT devices, computational-intensive tasks are difficult to be tackled locally. In this paper, a UAV-aided mobile edge computing (UAMEC) system is constructed to assist IoT devices to tackle computational-intensive tasks. Furthermore, we jointly optimize communications, computing and caching resources allocation strategies for the sake of maximizing the net revenue from the UAMEC under the condition of guaranteeing user’s quality of experience (QoE). A multi-dimensional hybrid adaptive particle swarm (MHAPSO) algorithm is conceived to solve this joint optimization problem. Finally, the effectiveness and superiority of our proposed scheme are demonstrated.
Spatial Shift Phenomenon Compensation for TomoSAR Imaging Using High-Resolution TerraSAR-X Data

Massive high-resolution spaceborne synthetic aperture radar (SAR) data are available nowadays, effecting rapid development in the urban remote sensing technology. In this study, we use a set of 27 scenes from TerraSAR-X to reconstruct the three-dimensional (3D) point clouds of a skyscraper—North Star Times Tower in Beijing, China. We study in detail the derivation process of a 3D model of tomographic SAR (TomoSAR) from the two-dimensional (2D) imaging mathematical model of SAR. We observe the presence of a spatial shift in the elevation direction of TomoSAR, which yields poor and even incorrect results when greedy algorithms such as orthogonal matching pursuit (OMP) or regularized orthogonal matching pursuit (ROMP) algorithms are applied. Then, we propose a phase compensation method to eliminate the spatial shift. Both OMP and ROMP algorithms are used for TomoSAR imaging on high-resolution TerraSAR-X data. The experimental results with North Star Times Tower demonstrate the accuracy of the theoretical analysis and the effectiveness of the algorithms. Finally, we compare the ROMP and OMP algorithms and conclude that both algorithms can yield similar reconstruction results in TomoSAR imaging. The ROMP algorithm does not depend on sparsity and can suppress noise through regularization selection at the cost of time. The OMP algorithm introduces noise when the sparsity is inaccurate.
Smart Grid and Cybersecurity Challenges

In the modern and computerized energy industry cyber security plays a critical role alongside physical security. The smart grid is considered as one of the leading revolutionary innovations in this industry with established solutions and engineering sciences that support the efficiency, flexibility and reliability of power, gas and water systems. A cybersecurity breach in this industry can have irreversible consequences resulting in disasters that threaten human life, along with more tradition consequences in security principles such as confidentiality, integrity and availability of the service or data. This paper seeks to describe the cyber security issues present in smart grids, including past challenges and their root causes, and proposes methodological approaches to protect the smart grid against cyber security attacks by proposing potential mitigating controls.
Performance Analysis of SDN and NFV enabled Mobile Cloud Computing

Mobile Cloud Computing (MCC) is regarded as a promising method to increase the data storage and enhance the processing power of mobile devices. Technologies such as Software Defined Networking (SDN) and Network Function Virtualisation (NFV) will be deployed in MCC to simplify the network management and accelerate mobile service deployment. In order to achieve a deeper understanding of future MCC, we developed a comprehensive analytical model to investigate the performance of MCC in the presence of both NFV service chains and SDN networks. The model is capable of capturing the interactions between SDN and NFV when they share the same underlying physical infrastructure. The end-to-end latency is derived for different scales of service deployments and network configurations. Comprehensive simulation experiments are conducted and the results demonstrate that the proposed analytical model corresponds well with the simulation experiments. In addition, we show how the analytical model can be a useful tool to investigate the impact of centralised SDN control on the performance of NFV traffic transmission.
Development of palm oil‐based alkyds as UV curable coatings

Purpose – The purpose of this paper is to develop palm oil‐based alkyds as ultraviolet (UV) curable coatings and investigate the parameters that affect the coating performances.Design/methodology/approach – Alkyds were formulated from palm stearin, glycerol, phthalic anhydride and maleic acid. Keeping the total molar amount of dicarboxylic acids constant, the proportions of maleic acid and phthalic anhydride were varied in order to produce polymer chains with different content of unsaturation, which is crucial for UV curability. Characterisations were carried out by FTIR and 1HNMR. The alkyds were then mixed with methyl methacrylate (MMA) monomer as active diluents and cured by exposure to UV light. Performances of the cured coatings were tested in terms of film hardness, adhesion, water and alkali resistance, and thermal stability.Findings – Upon introducing sufficient C=C, the alkyd in combination with MMA is able to UV‐cure within short time and produce film of satisfactory quality. There are several o.
A Comprehensive Detection Approach of Wannacry: Principles, Rules and Experiments

Recently, cybercrime has become an important research issue in the field of cybersecurity. And ransomware is considered one of the main categories of cybercrime. The Wannacry ransomware attack broken out in 2017 is one of the largest ransomware attacks in history, creating huge threats to the data security of individual users and enterprises. Since 2017, although the principle of Wannacry has been figured comprehensively and numerous detection and defense methods have been proposed, all of these existing methods have some defects. Therefore, we propose to build a comprehensive detection approach of Wannacry with a set of rules, which is named Comprehensive Wannacry Detection Rules (CWDR). Through principle analysis, we extract the traffic features. And these features can be easily distinguished from other normal traffic information during the Wannacry attack. Base on these traffic features, a set of rules is designed to detect the attack process of Wannacry. CWDR rule set can detect Wannacry at all 8 stages through network traffic monitoring. Finally, we instantiate the rule set using Suricata and do benchmark experiments with ET Open rule set and Snort rule set. In the basic experiment, the False Negative Rate of the CWDR rule set, ET Open rule set, and Snort rule set are 0%, 25%, 50%. In the evasion experiment, the False Negative Rate of the CWDR rule set, ET Open rule set, and Snort rule set are 12.5%, 75%, 62.5%. The results show that the CWDR rule set can detect the attack process of Wannacry ransomware more precisely.
Mitigating Cross-Core Cache Attacks via Suspicious Traffic Detection

Continuous Attacks are common cross-core cache side-channel attack scenarios that we observed, where adversaries frequently probe-target cache lines in a short time. Under Continuous Attacks, the attacked lines go through multiple load-evict processes between different cache (or memory) hierarchies, exhibiting Ping-Pong patterns. Identifying and obscuring these abnormal patterns effectively interfere with the attacker’s probe and mitigate such attacks. Our recent proposal, Ping-Pong regulator (PPR), captures multiple Ping-Pong patterns by counting the reaccesses per cache line and blocks them with different obscuring actions (preload or lock). Although PPR mitigates Continuous Attacks, the added regulator directory (RDir) is vulnerable because it cannot record all cache lines simultaneously. Sophisticated attackers can evict the records of the attacked line from the RDir to avoid triggering defensive actions, thereby bypassing PPR. To improve robustness, we further propose PPR+, which dynamically changes the mapping of physical addresses to RDir locations by encryption and periodically changing keys. This randomness makes it difficult for attackers to evict target entries out of the RDir within a limited time. We show that PPR+ tolerates more than 100 years of attacks, induces negligible performance impacts (improves 0.13%), requires acceptable storage overhead (3.15%), and does not need any software support.
On the Design of Generalized LDPC Codes with Component BCJR Decoding

Generalized low-density parity-check (GLDPC) codes, where the single parity-check (SPC) nodes are replaced by generalized constraint (GC) nodes, are known to offer a reduced gap to capacity when compared with conventional LDPC codes, while also maintaining linear growth of minimum distance. However, for certain classes of practical GLDPC codes, there remains a gap to capacity even when utilizing blockwise decoding algorithm at GC nodes. In this work, we propose to optimize the design of GLDPC codes where the GC nodes are decoded with a trellis-based bit-wise Bahl-Cocke-Jelinek- Raviv (BCJR) component decoding algorithm. We analyze the asymptotic threshold behavior of GLDPC codes and determine the optimal proportion of the GC nodes in the GLDPC Tanner graph.We show significant performance improvements compared to existing designs with the same order of decoding complexity.