Cyber security for power systems is becoming a concern for many researchers. However, the impact of cyberattacks on actual power systems is unclear. This paper discusses analytically the significance of cyber security for power systems and evaluates whether cyber-attacks are a real threat, or are just a hypothetical concept. First, this paper categorizes the different types of power system attacks and then systematically reviews the cyber-attack modeling, detection, and mitigation approaches for bulk power systems. This paper also discusses prominent cyber and physical attacks on actual power systems and draws a comparison among them based on economical and societal impacts. Based on the facts uncovered in the paper, when compared to physical attacks, cyber-attacks on power systems have had negligible economic and societal impacts. In fact, there is only one documented case (the Ukrainian cyber-attack by Russia) that successfully caused customer disconnections.

This letter analyses the achievable performance of Low Earth Orbit (LEO) Position-Navigation-Timing (PNT) using the novel Orthogonal Time Frequency Space (OTFS) modulation. While originally designed for communication purposes, we show the advantages this modulation may bring to the PNT domain, in particular for doubly-selective channels exhibiting both delay and Doppler spreads. We show how the LEO channel fits this description due to its fast dynamics, and we present the improvements of using OTFS with respect to the standard Binary Phase Shift Keying (BPSK) modulation in terms of delay-Doppler estimation accuracy, in addition to a comparison of their respective Multipath Error Envelopes.

A load-independent constant current (CC)-constant voltage (CV) output is an important requirement of inductive power transfer (IPT) systems for electric vehicle charging applications. Zero phase angle (ZPA) is also a desirable feature, to ensure a lower power rating requirement for the switching converter. CC and CV output along with ZPA can be achieved by using a suitable compensation topology. Equation manipulation techniques can be used for designing the compensation topology. But, it can be mathematically intensive especially for higher order topologies. To overcome this problem, resonant-tank based approaches are adopted in several works to achieve CC and CV conditions. However, equation-based approaches are depended upon either wholly or partly for realizing ZPA. This approach can be tedious and lacks physical insight. The proposed method extends resonant tank approach to achieve ZPA also, besides CC and CV. The need for a separate method to achieve ZPA is eliminated. Further, it simplifies the process in arriving at the constraints that ensure ZPA. As a sample validation, the proposed method is applied to a S-SP compensation topology. The CC-ZPA and CV-ZPA constraints for the S-SP topology are shown to be in line with the ones arrived at using an existing equation-based impedance approach. The simplicity of the proposed method can be observed from the sample validation.

In this work, we propose a novel training paradigm designed to support transfer learning for more effective classification in multispectral airborne imagery. Current state-of-the-art approaches typically rely on either leveraging solely RGB (red-green-blue) pretraining or applying in-domain transfer learning for multispectral imagery classification. Instead, our approach constructs and trains two separate neural network models (backbones): one specifically for wavelengths with available pretrained data (like visible bands) and another trained from scratch on all bands available in the dataset. These models are then integrated with a fully-connected layer or multi-layered perceptron, which is trained on the features from both networks. This allows us to exploit the significant benefits of generalizable features learned from RGB datasets and the information provided by the full spectrum of multispectral bands. We employ the BigEarthNet and EuroSAT datasets, encompassing Sentinel-2 satellite imagery in the visual and infrared bands. This approach yields considerable performance gains across every evaluation metric we utilized for these datasets. The results are also consistent across a variety of backbone architectures, underlining the efficacy of our transfer learning technique in the analysis of multispectral data.

Driver emotions play an important role in driving behaviour. Understanding how human emotions affect driving behavior is essential to enhance modern driver support systems, which aim at adjusting the mental and emotional states of a driver in order to improve road safety. In this paper, we first discuss the latest work on how to enhance driver emotions and how to improve driving habits with human emotion regulation, through the analysis of facial expressions, emotions, and driving behaviours of the subjects during different driving situations. Then, we study various driving support systems, and look at current findings and into potential applications of artificial intelligence for road safety via driver emotion regulation. Finally, further research directions are identified, and conclusions are drawn.

The aim of our research is to establish relationship between linearly independent vectors and the R3 Space The means for that are going to be taken from Balanced Ternary analysis e.g. triplets with entries (-1,0,1) This is not going to be any specific vector, for instance (-1,0,1) It’s going to be rather a Coordinate system with 8 quadrants and directional vectors The novelty of the proposed research lies in the fact that unlike traditional Cartesian system-vector basis our system uses 8 vectors which are at the same time a polyvector (Boas) We may define it as a multiple of the type:  ∑ (i=-1)^1∑ (j=-1)^1 ∑ (k=-1)^1〖(i,j,k)〗  

The 3D point cloud plays a crucial role in deep learning-based vision tasks by providing precise spatial and depth information, leading to its increasing importance in various applications. However, the sparse nature of 3D point clouds poses computational challenges. Researchers have explored the Submanifold Sparse Convolutional Network (SSCN) for processing point cloud data while preserving sparsity. Nevertheless, existing Convolutional Neural Network (CNN) accelerators encounter difficulties in effectively handling SSCNs, prompting recent studies to focus on developing dedicated accelerators for point cloud networks to improve processing performance. This brief presents a specialized hardware architecture designed for SSCNs to address the challenges of effectively processing sparse 3D point clouds. The proposed accelerator achieves a significant 2.51× improvement in throughput density compared to previous works, highlighting its effectiveness in point cloud processing.

Rice’s characteristic function method of analysis for combinations of signals and noise in a memoryless nonlinear system is extended in this paper to greatly facilitate numerical methods of solutions. This improvement is made by describing the nonlinearity as a Fourier series instead of a Fourier transform, as in the original method, thereby producing expressions for calculating the desired output correlation functions as summations rather than double or triple improper integrals that must be estimated. Even with modern computing tools, it has been found that estimating the values of the former improper integrals can be problematic for SNR conditions of particular interest that motivate the use of the characteristic function method, producing unreliable results and prompting researchers to look elsewhere for a solution. The modified method presented in this paper makes this powerful analytical tool more accessible to researchers and engineers, particularly as new semiconductor devices are under development requiring noise analysis. The extended method retains the useful properties of the original, including applicability to one or more signals (sine waves) and to any noise distribution since its characteristic function always exists, particularly Gaussian or random-telegraph shot noise. An alternative fundamental formula for the characteristic function method is presented expressed in terms of the Fourier coefficients and a discrete parameterization of the general characteristic function. New equations are presented and some numerical results for an example nonlinearity with a sinusoid plus Gaussian noise. The new procedure is concisely summarized for application to other problems.

Convolutional neural networks (CNNs) models play a vital role in achieving state-of-the-art performances in various technological fields. CNNs are not limited to Natural Language Processing (NLP) or Computer Vision (CV) but also have substantial applications in other technological domains, particularly in cybersecurity. The reliability of CNN's models can be compromised because of their susceptibility to adversarial attacks, which can be generated effortlessly, easily applied, and transferred in real-world scenarios. In this paper, we present a novel and comprehensive method to improve the strength of attacks and assess the transferability of adversarial examples in CNNs when such strength changes, as well as whether the transferability property issue exists in computer network applications. In the context of our study, we initially examined six distinct modes of attack: the Carlini and Wagner (C&W), Fast Gradient Sign Method (FGSM), Iterative Fast Gradient Sign Method (I-FGSM), Jacobian-based Saliency Map (JSMA), Limited-memory Broyden fletcher Goldfarb Shanno (L-BFGS), and Projected Gradient Descent (PGD) attack. We applied these attack techniques on two popular datasets: the CIC and UNSW datasets. The outcomes of our experiment demonstrate that an improvement in transferability occurs in the targeted scenarios for FGSM, JSMA, LBFGS, and other attacks. Our findings further indicate that the threats to security posed by adversarial examples, even in computer network applications, necessitate the development of novel defense mechanisms to enhance the security of DL-based techniques.

In the computer world, ransomware continues to evolve as a significant threat to cybersecurity, employing increasingly sophisticated techniques to evade detection and cause widespread disruption across industries. The novel approach presented in this research leverages opcode sequences combined with machine learning to address the limitations of traditional static analysis methods, offering a more robust and accurate detection mechanism. Through the use of t-SNE for dimensionality reduction and SVM for classification, this method efficiently handles high-dimensional opcode data, allowing for precise identification of ransomware samples. The experimental results show substantial improvements in detection accuracy, precision, and F1-score compared to baseline models, while demonstrating the importance of feature reduction in optimizing performance. Despite the computational overhead introduced through t-SNE, the overall findings indicate that the proposed framework enhances the static detection of ransomware, making it a valuable tool for cybersecurity efforts aimed at mitigating evolving threats.

The reachable and feasible sets of spacecraft are important tools for many areas of astrodynamics such as mission design, space situational awareness, and assessment of potential threats. Whilst these may be obtained by solving many optimal control problems in the indirect formulation, this process is slow and can suffer from convergence issues with a lack of a good initial guess. This work presents an analytical method of rapidly estimating the reachable and feasible sets utilizing the D matrix, an error state transition matrix under linearity assumptions for Keplerian motion which employs time as a state variable and true anomaly as the reference variable. Analytical expressions for the control laws are derived to produce the maximum/minimum change in orbital radius, time-offlight, and out-of-plane height. Moreover, an analytical expression for the control efficiency is derived to allow thrust activation only when it is efficient to do so, allowing a target fuel consumption to be attained. By mixing the contributions of these controls, the complete reachable or feasible sets can then be rapidly swept over with minimal effort, which is demonstrated in a series of numerical simulations. The results show that the optimal control problem solution presents only minor improvements, highlighting the capacity of the D matrix to provide marginally sub-optimal results within a fraction of the computational time.

Residual generation and hypothesis test are two important components in fault detection techniques. Recent studies mainly focused on enhancing residual generation algorithms, but often overlook the Gaussian distribution assumption that necessary for hypothesis test. Based on the conditional invertible neural network (CINN), this study proposes a novel approach for mapping residual signals into near-Gaussian distributed latent variables, thereby enhancing the reliability and effectiveness of the hypothesis test approaches used for fault detection. With the specially designed architecture using CINN, the proposed mapping from residual signals to latent variables has no information loss, thus guaranteeing the accuracy of the proposed fault detection method. The main contributions of this study are two-fold: 1) To ensure that the latent variables are distributed similarly to an ideal Gaussian distribution, a novel CINN training approach is proposed; 2) historical process information is incorporated into the residual-to-latent variable mapping, dynamically refining the mapping procedures in response to the system behavior. This approach is primarily used to tackle the challenges posed by non-additive and non-Gaussian noises in industrial systems. A DC speed control system and a waste water treatment system are adopted to verify the effectiveness of the proposed fault detection approach.

Food freshness classification is a growing concern in the food industry, mainly to protect consumer health and prevent illness and poisoning from consuming spoiled food. Intending to take a significant step towards improving food safety and quality control measures in the industry, this study presents two models based on deep learning for the classification of fruit and vegetable freshness: a robust model and an efficient model. Models' performance evaluation shows remarkable results; in terms of accuracy, the robust model and the efficient model achieve 97.6% and 94.0% respectively, while in terms of AUC score, both models achieve more than 99%, with the difference in inference time between each model over 844 images being 14 seconds.

In this work, the problem of predicting a pedestrian’s intention to cross the road is addressed using visual data captured from a camera. The proposed ROS-based modular architecture consists of four modules starting with the Visual-Perception, Intention Prediction, and the Planning and Control Modules. The visual perception is further divided into three sub-modules. First, pedestrian detection is responsible for detecting the pedestrian and analyzing his state using motion and looking classifiers. Secondly, the detection of the lane that is responsible for analyzing the structured environment which helps in the road state classifiers. The third sub-module aims to extract some curvilinear localization states that are essential for the vehicle’s motion planning and control. The intention prediction module is integrated to capture the pedestrian’s intention to cross the road. In this module, a comparative study is conducted between three different data-driven sequential models. Each model is trained on the JAAD dataset and different extracted features form the visual perception module. It is observed that the proposed GRU model obtained 86% average f1-score, and can predict a pedestrian’s intention three seconds before crossing. In order to control the maneuver of the vehicle, the Proportional-Integral (PI) controller is implemented for longitudinal velocity control to brake the vehicle to avoid collision with the pedestrian, and a Linderoth controller is used to control the lateral motion of the vehicle. Finally, this work is verified on a 1:4 scaled real vehicle to ensure the applicability of implementing this work in real hardware.

The 1-bit band-pass delta-sigma modulator (BP-DSM) achieves high resolution by using the oversampling technology. It can output a radio frequency signal directly from the digital components without using an analog frequency converter and local oscillator, thereby enabling a reduction in the size of the wireless equipment. On the other hand, this modulator itself is intrinsically difficult to reduce in size while increasing speed by time division multiplexing and parallel processing, because the modulator is composed of a feedback loop and forms a double feedback loop when the loop filter is used as an infinite impulse response filter. This paper introduces a noise transfer function NTF(z M) applying multiplicity M, and shows that time division multiplexing can be realized with a constant circuit size (number of multipliers and adders) and only additional memory. In addition, by distributing the additional memory, it is demonstrated that the critical path can be divided and the processing speed can be doubled from 114 MHz to 239 MHz, by designing an FPGA when M is two (two times multiplexing). Furthermore, it is demonstrated that the proposed modulator can perform concurrent multi-band transmission processing and/or MIMO system by multiplexing a 1-bit BP-DSM.

A document by Marika Kulmar. Click on the document to view its contents.

This paper introduces a preliminary concept aimed at achieving Artificial General Intelligence (AGI) by leveraging a novel approach rooted in two key aspects. Firstly, we present the General Intelligent Network (GIN) paradigm, which integrates information entropy principles with a generative network, reminiscent of Generative Adversarial Networks (GANs). Within the GIN network, original multimodal information is encoded as low information entropy hidden state representations (HPPs). These HPPs serve as efficient carriers of contextual information, enabling reverse parsing by contextually relevant generative networks to reconstruct observable information. Secondly, we propose a Generalized Machine Learning Operating System (GML System) to facilitate the seamless integration of the GIN paradigm into the AGI framework. The GML system comprises three fundamental components: an Observable Processor (AOP) responsible for real-time processing of observable information, an HPP Storage System for the efficient retention of low entropy hidden state representations, and a Multimodal Implicit Sensing/Execution Network designed to handle diverse sensory inputs and execute corresponding actions. By combining the GIN paradigm and GML system, our approach aims to create a holistic AGI system capable of encoding, processing, and reconstructing information in a manner akin to human-like intelligence. The synergy of information entropy principles and generative networks, along with the orchestrated functioning of the GML system, presents a promising avenue towards achieving advanced cognitive capabilities in artificial systems. This preliminary concept lays the groundwork for further exploration and refinement in the pursuit of true brain-like intelligence in machines.

Thermal to visual cross-domain face recognition is the process of recognising faces captured in the thermal domain and matching them with the visual domain images. Its significance lies in enabling accurate face recognition across different domains, allowing for enhanced security and surveillance capabilities in various environments, particularly during night-time or adverse weather conditions where thermal imaging excels. By bridging the gap between thermal and visual domains, this cross-domain face recognition improves the overall performance and applicability of face recognition systems in real-world scenarios. This survey explores the existing literature on thermal-to-visual cross-domain face recognition, categorising it into two distinct approaches: the first classification is based on the learning model, and the other is on generalisation approaches. This survey aims to provide a nuanced understanding of the current landscape, methodologies, and challenges in this evolving field by systematically reviewing and categorising the extensive body of research in thermal-to-visual cross-domain face recognition.