A common theme in all the above areas is designing a dynamical system to accomplish desired objectives, possibly in some predefined optimal way. Since control theory advances the idea of suitably modifying the behavior of a dynamical system, this paper explores the role of control theory in designing efficient algorithms (or dynamical systems) related to problems surrounding the optimization framework, including constrained optimization, optimization-based control, and parameter estimation. This amalgamation of control theory with the above-mentioned areas has been made possible by the recently introduced paradigm of Passivity and Immersion (P&I) based control. The generality and working of P&I, as compared to the existing approaches in control theory, are best introduced through the example presented below.

Achieving high-quality radar forward-looking imaging (FLI) presents significant challenges due to the poor azimuth resolution, which is constrained by the physical antenna aperture size and specific imaging geometry. In this paper, an efficient super-resolution FLI method that integrates wavefront modulation with beam scanning is proposed. Without resorting to complex super-resolution algorithms to solve the imaging equation, this method generates a distinct echo structure and then enhances resolution by extrapolating the azimuth information of the targets. First, a novel chirp-modulated beam is developed, functioning as a chirp pulse propagating along the azimuth direction during scanning. By leveraging the flexibility of modulation, chirp beams with varying azimuthal chirp rates are produced and employed successively in the scanning process, resulting in a series of two-dimensional (2-D) echo matrices. Subsequently, these matrices are compiled into a threedimensional (3-D) echo cube, which provides three degrees of freedom derived from the transmitting signal's bandwidth, beam scanning, and modulation parameters. Moreover, a maximum a posteriori (MAP)-based signal extrapolator is applied along the modulation dimension to augment target's azimuth information and enlarge the separability between different targets. Finally, super-resolution azimuth profiles are obtained through applying the Fourier transform (FT) along the scanning dimension to the extrapolated signal. Simulation results verify the effectiveness of the proposed method, showing a twofold to fourfold enhancement in azimuth resolution. Furthermore, a case study on sea-surface target imaging confirms its practical utility.

A document by Prarthana Pillai. Click on the document to view its contents.

Microservices-centric in-network computations, coupled with Named Data Networking (NDN) and supported by vehicular edge computing (VEC), offer a promising platform to meet the requirements of vehicular applications. However, a significant obstacle hindering high efficiency is the interdependency of microservices (MS), which may delay the timely results delivery due to pending interest table (PIT) timer expiration resulting in unsolicited packet drops. Moreover, voluminous data transmission in resource-constrained environment may prevent the consumer from receiving timely results. Therefore, to avoid unsolicited computation losses and enable proximate computations, this article envisions interdependent microservices offloading and semantic aware results transmission (iMSoRT) for 6G vehicular edge networks. iMSoRT formulates an efficient strategy that allows traffic controller (Tc) to account for MS interdependencies and allocate the PIT timer based on computational and network resource requirements. Moreover, iMSoRT introduces a live forwarding information base (lFIB) that effectively filters out underutilized RSU-E servers and forwards computation requests to the optimal RSU-E, accelerating processing while minimizing communication costs. Furthermore, iMSoRT develops a semantic transformer (ST) that leverages the YOLO and convolutional neural networks (CNN) and places it between the caching and forwarding module of NDN. The ST extracts and forwards semantically meaningful information, thereby reducing network resource utilization and enhancing information exchange efficiency. Simulation results revealed that iMSoRT achieved an impressive compute-hit ratio of over 90%, restrict cloud offloading to 12%, reduced computation delays over 50%, and optimized bandwidth utilization over threefold compared with benchmark schemes.

Significant advancements have been achieved in Brain-Machine Interface (BMI), particularly in electroencephalography (EEG)-based systems, which capture the brain’s electrical activity dynamics non-invasively. This study focuses on EEG-based BMI for detecting voluntary keystrokes, aiming to develop a reliable brain-computer interface (BCI) to simulate and anticipate keystrokes, especially for individuals with motor impairments. The dataset, featured in a Nature publication, was initially band-pass filtered and segmented into 22-electrode arrays, and our work began with excluding non-significant channels (A1, A2, and X5), and the feature-extracted dataset was utilized for developing models. Using ERP window-based segmentation, each window was categorized relative to Event 0, resulting in a 19*200 data array of features set. The ERP window-based data segmentation was done with those below Event 0 belonging to one and above Event 0 belonging to another window so that total electrode data division resulted in a total of 19*200 data arrays to begin model development. The methodology includes extensive segmentation, event alignment, ERP plot analysis, and signal analysis. Different deep learning models are trained to classify EEG data into three categories—’resting state’ (0), ‘d’ key press (1), and ‘l’ key press (2). Real-time keypress simulation based on neural activity is enabled through integration with a tkinter-based graphical user interface. Feature engineering utilized ERP windows, and the SVC model achieved 90.42% accuracy in event classification. Additionally, deep learning models—MLP (89% accuracy), Catboost (87.39% accuracy), KNN (72.59%), Gaussian Naive Bayes (79.21%), Logistic Regression (90.81% accuracy), and a novel Bi-Directional LSTM-GRU hybrid model (89% accuracy)—were developed for BCI keyboard simulation. Finally, a GUI was created to predict and simulate keystrokes using the trained MLP model.

A document by Neda Shahidi. Click on the document to view its contents.

This study deals with flexibility quantification from space and water heating in about a million houses equipped with heat pumps, for providing emergency power support in a renewable dominated power-system. As electricity consumption determines the flexibility potential, this is estimated using integrated physics-based models involving a heat pump, space and water heating. The flexibility estimations are between 2.1 GW and 0.5 GW, at outdoor ambient temperatures ranging between -10◦C and 10◦C respectively, for a power-system with a dimensioning fault of 1.45 GW. The reduction in electric energy is between 5.4 GWh and 3.2 GWh, when the indoor and water temperatures are reduced from 20◦C and 55◦C, to 18◦C and 50◦C respectively over 17.25 hours, considering 24 hours recovery period. Additionally, electricity consumption as a function of time serves as valuable information for power balancing. Furthermore, a modified Nordic-32 bus system with a high share of renewable power installations is proposed. In this system, the loss of a major generation corresponding to 12% of the load, causes an instantaneous frequency deviation below 48 Hz. This can be limited by providing emergency power support using house heating flexibility corresponding to at least 5.9% of the load.

Ransomware has become a pervasive threat in the cybersecurity landscape, causing significant financial and operational damage across various sectors. Despite substantial research focused on well-known ransomware families, less commonly labeled variants remain underexplored, representing a hidden yet critical facet of the threat spectrum. This research presents a comprehensive analysis of less commonly labeled ransomware families, employing advanced clustering techniques, similarity metrics, and pattern recognition to uncover evolutionary connections and shared characteristics with more prominent ransomware. The study's findings reveal that even lesser-known ransomware can exhibit sophisticated behaviors and adaptation strategies, emphasizing the need for inclusive cybersecurity approaches that encompass the full range of ransomware threats. Through a novel methodological framework, the research highlights the effectiveness of machine learningbased detection techniques in identifying both common and obscure ransomware variants, thus contributing to the development of robust and adaptive cybersecurity defenses. By providing a deeper understanding of ransomware evolution and behavior, the study enhances the ability to anticipate and mitigate future threats, promoting a more resilient cybersecurity environment.

Predictive Control optimization typically relies on exhaustive enumeration of all possible voltage vectors within the finite control set. For simpler implementation the computational burden is not a concern, but the limited control set can demand a very high sampling frequency to reach good steady state performance. Indirect Predictive Control or Modulated Predictive Control addresses this by using a modulation stage, but classic implementation depends on duty cycle calculation that can limit constraints and non linearities in the cost function. This paper introduces the Informed Binary Search, an intelligent search algorithm capable of handling any constraints within the cost function, similarly to Finite Control Set, but with the benefit of fixed switching frequency. Experimental results on a two-level VSI driving an induction motor is shown but the method can be applied to every setup of machine and inverter. The results are compared with PTC, PTC with DSVM and MPTC and the metrics show improvements in torque and flux ripple and improvements of current THD up to 30% over classic M2PC while having similar computational burden.

Accurately parameterizing human gait cycles is crucial for developing control systems for lower-limb wearable robots. Previous studies on characterizing human gait cycle investigated the use of human-inspired phase variables, but they lack robustness and adaptability to accommodate for transitions that occur between locomotor tasks. This paper proposes augmenting the traditional phase variables with a canonical dynamical system. This system considers the differences between the estimated and true thigh angular velocities, based on which the Fourier series coefficients and phase information will be dynamically updated via an adaptive frequency oscillator. Experiments with six able-bodied participants on level ground, inclines, and declines walking show that the augmented phase variable adapts to various locomotor tasks and transitions better than the traditional approach.

Public-key Encryption with Keyword Search (PEKS) is a promising cryptographic mechanism that enables a semi-trusted cloud server to perform (on-demand) keyword searches over encrypted data for data users. Existing PEKS schemes are limited to precise or fuzzy keyword searches, creating a gap given the widespread use of wildcards for rapid searches in real-world applications. To address this issue, several wildcard keyword search schemes have been proposed to support wildcard searches in the public key setting. However, these schemes suffer from inefficiency and/or inflexibility. Worse yet, they are all vulnerable to (insider) keyword guessing attacks (KGA), which is highly effective when the keyword space is polynomial in size. To address these vulnerabilities, this paper first proposes a new wildcard keyword search scheme called Public-key Encryption with Wildcard Search (PEWS), which is built based on the standard Decisional Diffie-Hellman (DDH) assumption. The complexity of all algorithms in PEWS increases linearly with the number of keywords, while remaining almost constant or even decreasing linearly with the number of wildcards. To resist against (insider) KGA, we further extend PEWS into the first Public-key Authenticated Encryption with Wildcard Search (PAEWS) scheme. Our PEWS and PAEWS schemes are highly flexible, supporting searches not only for sets of keywords but also for any number of wildcards positioned anywhere within the keyword set. We conduct a comprehensive performance evaluation of our PEWS and PAEWS, while also comparing PEWS with the state-of-the-art scheme in the public key setting. The experimental results demonstrate that both PEWS and PAEWS are efficient and practical in computation and communication, and the experimental comparisons illustrate that PEWS achieves significant improvements in computational efficiency.

A black hole is an area in space with immense gravity that nothing (not even light) can escape from it. They are formed usually when a star dies. These black holes can be found by the detectors in space. They detect the gravitational waves by the ripples' effect. The isolated black holes that are invisible can be found using a method called gravitational lensing. Various black holes have been found using these methods. These black holes do not emit any light. This makes it difficult for scientists to observe the black holes directly. The effects of black holes on the nearby matter and light were observed by the scientists to detect a black hole. In this paper, a comprehensive review is presented about the techniques that can be used to detect and analyze black holes using machine learning. Information regarding the technique along with the conditions has been summarized in the paper. Other issues regarding the technology limitations, research challenges, and future trends are also discussed.

Multi-objective optimization is critical in SCM to balance conflicting objectives such as cost minimization and service level maximization. This paper presents a comprehensive study on the application of Simulated Annealing (SA) as a metaheuristic approach to address these challenges. The study includes detailed mathematical formulations, Python code implementation, and empirical evaluations using real-world SCM datasets to demonstrate SA's effectiveness and practical application.

Grid forming converters can offer fast voltage support during grid disturbances through a prompt injection of reactive current. Nevertheless, the large overcurrents experienced during grid faults can trigger the converter current limiter, which is often responsible of transient instability problems. While over-dimensioning the power rating of the converter enhances its grid supporting capabilities, it significantly increases the costs. Online monitoring of the semiconductor junction temperature, instead, enables the converter to perform a safe over-current operation for a limited time interval, potentially improving the transient stability without converter overdimensioning. Nevertheless, the transient stability analysis and current limitation design considering thermal dynamics is very challenging due to the high order of the model, which makes conventional methodologies based on second-order models not applicable. This article proposes a temperaturedependent current limitation in grid-forming converters, enabling overcurrent operation and significantly improving transient stability. A methodology for transient stability analysis is proposed based on numerical methods applied to the nonlinear state-space model of the converter under thermally stressed conditions during over-current scenarios caused by grid faults. The proposed method is implemented in a simulation of a grid-forming medium voltage Modular Multilevel Converter (MMC) considering the dynamics of junction temperatures.

Communication is crucial for Dual Side Control (DSC) of Inductive Power Transfer (IPT), which uses multiple variables in two separate converters for efficiency optimization. To avoid add-on communication systems, this letter proposes to implement Talkative Power Conversion (TPC)-added utility communication by modulated power conversion without intrusive changes to hardware, control or power conversion. The secondary converter is used to transmit feedback information and the Phase Locked Loop (PLL) typical of synchronous DSC is relocated to the primary side as the receiver, avoiding hardware additions. Based on Continuous Phase Frequency Shift Keying (CPFSK), the communication process is then designed to act as quasi steady state to the power transfer and control processes. The strategy is demonstrated to actively close a DSC loop in experiment with an average frequency shift of only 0.15 Hz. No added perturbations are visible in time domain voltage or currents, which confirms the nonintrusive nature of the proposed method.

In the rapidly evolving field of multimedia services, video streaming has become increasingly prevalent, demanding innovative solutions to enhance user experience and system efficiency. This paper introduces a novel approach that integrates user digital twins-a dynamic digital representation of a user's preferences and behaviors-with traditional video streaming systems. We explore the potential of this integration to dynamically adjust video preferences and optimize transcoding processes according to real-time data. The methodology leverages advanced machine learning algorithms to continuously update the user's digital twin, which in turn informs the transcoding service to adapt video parameters for optimal quality and minimal buffering. Experimental results show that our approach not only improves the personalization of content delivery but also significantly enhances the overall efficiency of video streaming services by reducing bandwidth usage and improving video playback quality. The implications of such advancements suggest a shift towards more adaptive, user-centric multimedia services, potentially transforming how video content is consumed and delivered.

Agile software development projects focus on running software [BBvB + 01]. Requirements engineering will be performed as far as it is required by the development team. The agile procedure seeks to reduce bureaucracy. However, a risk exists that a focus on the completion of development tasks leads to technical debt. Enhanced requirements engineering could face this risk. In order to reduce the risk of technical debt in agile projects the use of a code generator could be solution a more pragmatically. In this paper, a concept for a code generator will be elaborated.

This paper presents a design approach for dual-band dual-polarized (DBDP) waveguide antenna array. A novel transmission network using orthogonal parallel plate waveguide (PPW) is proposed for the excitation of dual polarization. Dual-band bandstop structures are incorporated into the PPW feeding network. They suppress the leakage between the two polarizations and improve the transmission efficiency of the network. They also enable a wide range of frequency ratio from 1 to 2.64 between the two bands. A new orthogonal linear signal generator (LSG) enables ultra-wideband, dual-polarized quasi-TEM excitation, ensuring consistent and reliable performance across a broad bandwidth. Fed by four sequentially rotated single-ridge 1-to-4 power dividers, the LSG effectively generates dual-polarized signals. For demonstration, a K/Ka-band shared-aperture dual-polarized array antenna has been designed, analyzed and experimentally verified. The array features a wide impedance bandwidth (17.7-21.2 GHz and 27.5-31 GHz), high isolation (>50 dB), and excellent cross polarization discrimination (XPD) (>40 dB). The prototype demonstrates high aperture efficiency up to 80% and consistent radiation patterns at both bands, affirming its potential for SATCOM systems.