The Gene Regulatory Network (GRN) in biological cells orchestrates essential functions for adaptation and survival in diverse environments, drawing on structural similarities with the Artificial Neural Network (ANN) to transform into the Gene Regulatory Neural Network (GRNN). This transformation enables exploration of their natural computing capabilities regarding network reconfigurability and controllability, facilitating dynamic adjustments of gene-gene interaction weights to regulate biological processes. In this paper, we present a control-theoretic model for the GRNN that determines optimal chemical input concentrations, steering the GRNN towards desired weight configurations using the Linear Quadratic Regulator (LQR) approach. This method enhances network robustness by optimizing stability and minimizing plasticity, ensuring adaptive reconfigurability in dynamic environments. We develop mathematical models to identify critical genes using a Continuous-Time Markov Chain (CTMC) and derive temporal weight configurations, providing insights into the system's reconfiguration dynamics, while also quantifying stability and plasticity. Our findings demonstrate the effectiveness of the control model in mitigating Clostridioides difficile biofilm formation, outperforming sub-optimal and stochastic inputs, and highlighting the importance of determining optimal inputs for robust network behavior across diverse complexities.

The interactions between geometrical Doppler, beam pattern, and normalized radar cross section (NRCS) result in leakage of geometrical Doppler into the perceived geophysical Doppler. Starting from high-resolution synthetic aperture radar (SAR) data we model this leakage for synthesized real aperture radar (RAR) observations of ocean motion. The uncertainty introduced by leakage is ∼1 m s −1. Corrections proposed in this work, using only the synthesized lower-resolution RAR NRCS, decrease this to O(0.1 m s −1). Further reduction of instantaneous leakage may be achieved through temporal averaging, since the leakage-inducing NRCS gradients appear mostly atmosphere induced and decorrelate rapidly. The azimuth resolution and number of independent samples determine a system's sensitivity to leakage. C-band systems are inherently prone to suffer from greater leakage and worse corrections than their Ku-and Ka-band counterparts. With DopSCA, the propagated effect of leakage is only secondary compared to the pulse-pair uncertainty. In similar systems where pulse-pair uncertainty is suppressed, leakage will dominate instead.This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Most of the publications on Quantum Computing and its main building block; the Qubit, rely on the Quantum Theory and use its terminologies. It is therefore not an easy task for the Electrical Engineers to fully understand the concept of the qubits, and how they are designed, controlled, readout, and used for computation. The primary aim of this paper is to show that the physical quantities describing the superconducting Transmons-the microwave realizations of the qubits-are just noisy signals similar to those encountered in e.g. Mobile and Satellite Communications. The nature of the noise in the qubit case, which will be simply called Quantum Noise, is essentially different from that of e.g. the thermal one. Contrary to the well-studied Thermal Noise, the Quantum one enjoys precise probability distributions and can interfere coherently. In particular, the so-called Stationary States resemble individually pure noise. However, a superposition of at least two of them results in a noisy signal, whose signal-to-noise ratio improves (goes higher) as more stationary states enter the superposition. Furthermore, the excitation (control) and readout of the qubits will be treated from the perspectives of the Electrical Engineering.

The escalating sophistication of ransomware attacks necessitates the development of innovative detection methodologies capable of identifying and mitigating such threats with high precision. The introduction of Quantum Heuristic Analysis (QHA) represents a significant advancement in this domain, leveraging quantum computational principles to enhance anomaly detection mechanisms. Through the integration of quantum heuristics, QHA effectively identifies complex data patterns and subtle anomalies, thereby offering a robust defense against evolving ransomware threats. Empirical evaluations demonstrate that QHA achieves a detection accuracy exceeding 98%, surpassing traditional signature-based approaches. Furthermore, the framework's design, which operates without human intervention, reduces the potential for human error and allows for continuous monitoring and detection, thereby enhancing the overall resilience of cybersecurity infrastructures. The scalability of QHA has been validated across varying dataset sizes, maintaining consistent performance metrics, and its adaptability to diverse encryption techniques demonstrates its robustness. These findings suggest that QHA holds substantial promise for integration into existing cybersecurity infrastructures, providing a proactive and efficient approach to ransomware detection.

The crucial element for ensuring secure optical communication lies in the consistent recovery of high-quality messages and validation needs to occur periodically. Semiconductor lasers have proven to be outstanding devices in the role of transceivers. This study delves into optical communication utilizing unidirectionally coupled multimode semiconductor lasers, specifically 3-mode lasers, as both transmitter and receiver. Two types of coupling are examined: global coupling, where the output of transmitter modes connects to corresponding receiver modes, and cross-modal coupling, which introduces a detuning equivalent to the frequency spacing of modes. The research is focused on investigating the impact of the transmission coefficient and bias current on the quality of the transmitted message. In the first coupling scenario, a high-quality message is successfully recovered, whereas the second coupling scheme demonstrates a neutral effect on message recovery quality. Message recovery is shown with an analog signal and a periodic bit sequence.

This paper investigates the motivations for developing underwater gait assistance robots and explores methods that leverage the benefits of partial submersion for rehabilitation. An overview of the current state of underwater gait rehabilitation robots is provided, highlighting the successes and limitations of existing designs. The study surveys key approaches in gait characterization, actuation, sensing, system modeling, and control strategies to identify those best suited for underwater environments. Emphasis is placed on how buoyancy and drag can be harnessed during hydrotherapy to improve gait rehabilitation outcomes. The review identifies research gaps and outlines future opportunities for designing effective underwater robotic systems that complement therapeutic needs.

In recent years, wireless sensing has become a significant research focus in the field of Internet of Things due to its advantages of wide sensing range, low deployment cost, and non-intrusive to user privacy. However, wireless signals are susceptible to environmental interference, and their sensing capability is highly coupled with the scenario, leading to limitations in robustness and scalability of existing systems. To address these challenges, one possible solution is to fully explore the collaborative sensing potentials of ubiquitous wireless sensing resources. To this end, we introduce the concept of "Crowd Wireless Sensing" for the first time, and propose a new wireless sensing framework named CrowdSignal, aiming to achieve robust and scalable wireless sensing based on the collaboration and complementarity of dynamic gathering sensing resources in real-world scenarios. Preliminary experimental results validate the importance and viability of the proposed framework. To further advance the research in this emerging field, we elaborate possible future directions at the end of the article.

The energy consumed by buildings is expected to significantly rise in the upcoming years, necessitating intelligent Home Energy Management Systems (HEMS) that create comfortable conditions for their inhabitants, while also offering sustainable and cost-effective solutions. The building environment, however, includes multiple time-varying parameters that cannot be controlled, such as the output of renewable energy sources, the market-dependent electricity prices, the outdoor temperature, as well as the occupants' energy habits. To overcome these barriers, we propose a hybrid Machine Learning (ML) algorithm for smart HEMS control, leveraging the properties of a decision-making deep deterministic policy gradient model, enhanced by the predictive capabilities of long short-term memory networks. Hence, the proposed algorithm aims to achieve an optimal balance between energy cost and occupant comfort by continuously adjusting the energy provided to the heating, ventilation, and air conditioning system, as well as controlling the energy storage system of the smart home. The proposed hybrid method is validated with simulations using real-world data and compared against baseline approaches, showcasing its effectiveness to achieve an optimal trade-off between the indoor temperature deviation and the average energy cost.

The transition of the web from centralized to decentralized or distributed architectures offers numerous advantages but also introduces significant challenges. One of the key challenges is user profiling to provide personalization, particularly personalized content recommendations. Traditional centralized recommendation systems rely on aggregated user data and central servers, making them incompatible with the principles of decentralization in Web 3.0. To bridge this gap, we propose D-RecSys, a decentralized recommendation framework specifically designed for Web 3.0-based content-sharing dApps. D-RecSys combines federated learning and clustering algorithms to deliver personalized recommendations while preserving user privacy and anonymity. The framework leverages blockchain technology for trustless coordination, enabling the generation of a global model through a modified block structure and mining algorithm. This structure facilitates the aggregation of local models into intermediate block models and subsequently produces the global model. To validate the effectiveness of D-RecSys, we conducted a number of experiments in a simulated Web 3.0 environment. The results demonstrate that D-RecSys achieves performance levels comparable to centralized recommendation systems while adhering to the core principles of decentralization, user anonymity, and data privacy.

Inverse synthetic aperture radar (ISAR) imaging of sparse aperture is a challenging problem. Noting the ISAR image generally exhibits strong sparsity, compressive sensing (CS) and sparse signal recovery methods are widely applied for sparse aperture data. However, the existing sparse aperture ISAR imaging algorithms are either computationally heavy or require manual adjustment of too many parameters, which limits their applications in the real-time ISAR imaging system. This paper proposes a high precision and computationally efficient ISAR imaging algorithm for sparse aperture. A generalized CS model with log-sum minimization is first considered for ISAR imaging, and then the iterative log-sum thresholding (ILT) algorithm is utilized to solve the optimization problem, where no large-scale matrix inversion is performed for reducing the computational overhead. Specifically, to accurately estimate ISAR image sparsity and avoid manually tuning parameters, a novel adjustment strategy of the trade-off parameter λ is applied for the ILT algorithm. To analyze the influence of different measurement matrix on the imaging algorithm, four sparse sampling patterns, including centralized sampling (CES), gap missing sampling (GMS), equally space sampling (ESS), and random missing sampling (RMS), are analyzed based on the mutual incoherence property (MIP). Experiments based on both simulated and measured data validate that the proposed algorithm can achieve well-focused ISAR images with a few seconds and is very efficient to implement.

This work introduces a novel concept for the realization of biological artificial neural networks (ANNs), called shared volume computing (SVC). The central units in this concept are biological cells, that function as individual neurons of the ANN. These cells reside inside a shared volume, where they sense signaling molecules released by cells of the previous layer or a biological process. They respond by the production of a chemical that they release into the shared volume, thereby creating the input into the next layer. We introduce the SVC, present mathematical models of it and show, how these models can be used to design a biological ANN for a given task. Specifically, we discuss the design process on a first example: breast cancer detection from biomarkers. We show in simulation, that the proposed system is capable of solving this task with an accuracy of up to 75%, making it especially useful for rapid testing.

We employ the Hopfield model as a simplified framework to explore both the memory deficits and the biochemical processes characteristic of Alzheimer's disease. By simulating neuronal death and synaptic degradation through increasing the number of stored patterns and introducing noise into the synaptic weights, we demonstrate hallmark symptoms of dementia, including memory loss, confusion, and delayed retrieval times. As the network's capacity is exceeded, retrieval errors increase, mirroring the cognitive confusion observed in Alzheimer's patients. Additionally, we simulate the impact of synaptic degradation by varying the sparsity of the weight matrix, showing impaired memory recall and reduced retrieval success as noise levels increase. Furthermore, we extend our model to connect memory loss with biochemical processes linked to Alzheimer's. By simulating the role of reduced insulin sensitivity over time, we show how it can trigger increased calcium influx into mitochondria, leading to misfolded proteins and the formation of amyloid plaques. These findings, modeled over time, suggest that both neuronal degradation and metabolic factors contribute to the progressive decline seen in Alzheimer's disease. Our work offers a computational framework for understanding the dual impact of synaptic and metabolic dysfunction in neurodegenerative diseases.

Existing control paradigms for lower-limb exoskeletons aim at replicating task-specific, subject-dependent reference kinematics, which overly constrain voluntary human motion. Individuals with voluntary control over their lower-extremities would likely benefit from the ability to choose their gait patterns freely while being assisted by exoskeletons during different activities. In this paper, we propose a novel control paradigm and its implementation to alter a human's Centroidal Momentum, i.e., a sum of projected limb momenta onto the human's center of mass, through tracking a dynamic reference Centroidal Momentum to provide consistent assistance across tasks. This reference Centroidal Momentum is defined based on the human's self-selected gaits with scaled anatomical parameters, rather than reference kinematics of specific locomotor tasks. The resulting control strategy does not prescribe any reference trajectories, providing flexibility for human users. We demonstrate simulation results on a human-like biped and experimental results on four human subjects wearing a bilateral hip exoskeleton performing various walking tasks under different speed and incline/decline conditions. These results show that the generated assistance/resistance can reduce/increase the subjects' muscle efforts, respectively, across the performed tasks.

In Newton's and Einstein's theory, all mass gravitates in an isotropic (spherical) manner. In this paper, we will consider ellipsoidal -- anisotropic -- gravitating processes. We discuss dark matter, as well as dark energy and the possibility of a final, 5th interaction.

Spin-orbit torque (SOT) has been extensively studied as a key mechanism in spintronics applications. However, conventional SOT materials limit the spin polarization direction to the in-plane orientation, which is suboptimal for efficient magnetization switching. Recently, multi-directional spins generated by low-symmetry materials have been observed, offering a promising energy-efficient strategy for the field-free switching of magnetic materials with perpendicular magnetic anisotropy. However, the efficiency of this mechanism is highly dependent on the crystallographic texture of the SOT materials, a factor that has not been systematically and numerically studied. Here, we investigate the impact of in-plane crystallographic orientations of SOT materials on the unconventional SOT generated by Dresselhaus-like and out-of-plane spin polarizations. By employing a theoretical orientation distribution function, we calculate the effective unconventional SOT values for SOT materials with tunable in-plane crystallographic texture. This analysis provides insights into the synthesis and optimization of future low-symmetry SOT materials, which can enhance operational efficiency for spintronics applications in magnetoresistive random-access memory and spin logic devices.

We demonstrate that data-driven system identification techniques can provide a basis for effective, non-intrusive model order reduction (MOR) for common circuits that are key building blocks in microelectronics. Our approach is motivated by the practical operation of these circuits and utilizes a canonical Hammerstein architecture. To demonstrate the approach we develop a parsimonious Hammerstein model for a nonlinear CMOS differential amplifier. We train this model on a combination of direct current (DC) and transient Spice (Xyce) circuit simulation data using a novel sequential strategy to identify the static nonlinear and linear dynamical parts of the model. Simulation results show that the Hammerstein model is an effective surrogate for the differential amplifier circuit that accurately and efficiently reproduces its behavior over a wide range of operating points and input frequencies.

In the evolving energy landscape where prosumers play an increasingly important role, establishing of an efficient data exchange architecture is essential for building a resilient and secure energy infrastructure. This paper introduces the Privacy-Sensitive Distributed Optimization (PSDO) framework, a decentralized management scheme designed to prioritize privacy safeguards in prosumer-driven systems. The primary objective of the PSDO approach is to empower prosumers, allowing them to autonomously manage their energy needs through decentralized decision-making. To achieve this goal, the PSDO framework combines decentralized optimization techniques with differential privacy. This integration serves a dual purpose: preserving prosumers' control over their energy management and ensuring privacy of their sensitive information. By leveraging decentralized optimization, the PSDO framework enables prosumers to maximize the benefits derived from decentralized systems, promoting improved autonomy in energy management. Simultaneously, prosumers' sensitive data is safeguarded through the implementation of differential privacy measures. Through extensive experiments on the IEEE 33-bus radial distribution system, the PSDO algorithm demonstrated its adeptness in converging to the optimal solution while rigorously upholding differential privacy.

In this study, the method used involves segmenting images into fixed-sized square regions called superpixels and then analyzing features of each superpixels to determine whether it belongs to the foreground or background. The study also discusses the process of selecting relevant features and testing different combinations. These features include characteristics of the superpixel itself—its local relationships with neighboring superpixels, and global features of the entire image. Importantly, it emphasizes making the decision-making process understandable—achieved by using a decision tree model for analyzing the collected features. This study focuses on teaching a self-driving system how to accurately separate foreground (movable areas like floors) from background (non-movable areas) in images captured inside buildings. This approach aims to strike a balance between entirely automated segmentation models like Convolutional Neural Networks (CNNs)—which lack explainability, and purely knowledge-based segmentation models, where all information is predefined. This information is stored in a database for analysis. Specifically, it addresses the scenario of an autonomous vehicle navigating public spaces without endangering people.