We present a dynamic antenna array system for vehicle-to-everything (V2X) communications applications that is capable of steering a spatially narrow information beam to provide increased wireless security at the physical layer. The array is based on a standard 1×2 patch antenna array with 0.5λ spacing with an amplitude imbalanced feed using a Wilkinson divider and an in-series double-pole-double-throw (DPDT) RF switch. Switching the outputs of the asymmetric Wilkinson divider rapidly between the two antenna inputs induces an additive dynamic modulation towards undesired directions. This dynamic modulation corrupts the data transferred through the channel by increasing the bit error ratio (BER) towards undesired directions, but maintain zero BER at the desired direction, referred to as the information beam. Traditional phase weighting is shown to steer the information beam to an arbitrary direction. Wireless communication security is demonstrated using a 1×2 patch antenna array designed at 2.5 GHz. The physical layer modulation scheme used is 16-QAM transmitting 48 kbit bursts. The data-stream is a pseudo-random bit sequence (PRBS) transmitting at a rate 4 Mbits/s. The DPDT RF switch was driven at a comparatively slow rate of 1 kHz. The communication system was operating at a minimum SNR of 25 dB so that any bit errors are due to the phase and amplitude dynamics, and not due to low SNR. The dynamic array obtained a low BER of < 10 −3 within an information beam of 25 • at broadside, and ∼ 45 • when the array is steered to ±30 • , while a traditional static array yielded low BER at all angles (no wireless security).

In this paper, an analysis and performance review of a unique hybrid high-power lithium-iron phosphate cell (HP-LFP) with a high cycle life and fast charge/discharge rate is presented. The new hybrid cell has been developed under the framework of the EU-funded project Hybrid Energy Storage Station (HEROES). The proposed HP-LFP cell simultaneously achieves an optimized energy and power throughput making it useful for applications where both power and energy are demanded such as different types of electric vehicles (EVs), microgrids, and charge stations. The cathode of the hybrid HP-LFP cell is similar to the LFP batteries while the supercapacitorlike anode is based on hard carbon. Thus, the HP-LFP cell has some similarities to the recent lithium-ion capacitors (LiCs) but it behaves differently than the existing LFP batteries and supercapacitors. To distinguish the differences, HP-LFP cell samples are tested, characterized, and analyzed to review their performance compared to the existing technologies. Different standard tests including the open-circuit test, hybrid pulse power characteristics test, and capacity test are fulfilled. The test data is used for model parameterization and parameter sensitivity analysis with respect to the cell's state of charge, temperature, and charge rate. The cell performance has been validated through mission profile testing in an EV fast charge station. Based on the analysis, the paper provides a comparison between different energy storage cells.

Hybrid Radio Frequency (RF) and Free Space Optics (FSO) communication systems represent a promising solution for modern communication challenges, offering the robustness of RF and the high data rates of FSO. As these systems become increasingly important in applications ranging from last-mile connectivity to satellite communication, ensuring their security, particularly at the physical layer, has emerged as a critical concern. This survey explores the state of the art in physical layer security (PLS) for mixed RF-FSO systems, highlighting key techniques, challenges, and solutions. We also outline future research directions to enhance the security and performance of these hybrid communication systems. By integrating insights from the latest research, this paper aims to provide a comprehensive understanding of the current landscape and future potential of PLS in mixed RF-FSO systems.

This project focuses on exploring the characteristics of the 74LS161 integrated circuit (IC) in a more specific set tailored for visual simulation. The exploration extends beyond theoretical analysis, incorporating practical experiments and simulations that highlight the IC's real-world features. Specifically, the project delves into timing analysis and the impact of load variations on the 74LS161's performance, which are crucial for understanding its operation in complex circuits. The article primarily discusses the principles of the 74LS161 IC digital logic, relevant features, and then applies the 74LS161 to a hexadecimal counter followed by simulations. The simulations are divided into two types: the first using NI Multisim software to generate circuit diagrams and perform static simulations, and the second involving code in Vivado Verilog to generate dynamic waveforms for simulation. The visual results of these two simulation methods are compared and evaluated against the actual properties and minor applications of the 74LS161 IC.

Diabetic Retinopathy (DR) and Glaucoma (GL) are critical eye diseases that can lead to vision loss if not identified early. Manual diagnosis using fundus images is time-consuming and error-prone, highlighting the need for automated methods for efficient and accurate classification. Deep learning models improve diagnostic performance but still face challenges such as class imbalance and similarities between disease stages, which complicate early detection.To address these challenges, we propose an Enriched Feature-based Multi-Class (EFMC) deep learning classifier for detecting DR and GL from fundus images. The EFMC classifier operates in three stages: extracting domain-specific features through unsupervised learning, enriching these features with multi-scale filters, and classifying them using multiple CNN classifiers with a hybrid loss function. This approach effectively tackles class similarity and imbalance. Experimental results on benchmark datasets show that the EFMC system outperforms state-of-the-art models, achieving 98.75% accuracy and a 98% F1-score.

The ongoing energy transition introduces challenges in designing energy systems, particularly at the low-voltage level, where additional loads like electric vehicle charging points and heat pumps are predominantly connected. Obtaining accurate grid data for modeling and analysis is hindered by availability and confidentiality concerns, especially for low-voltage distribution grids. Unlike high voltage grids, low-voltage grids lack standardized handling due to their large size and heterogeneity.This research addresses an essential gap in the availability of grid data, offering a methodological base to generate synthetic low-voltage grids based on geospatial data. This includes the consideration of multiple feeders per transformer, greenfield and brownfield transformer positioning, and variable dimensioning of equipment components. Based on data clustering methods and an algorithmic graph-based approach, the tool provides a scalable solution that is applicable to large areas such as cities, states, or even countries with hundreds of thousands of grids.The derived representative synthetic grid topologies support the development of more resilient and efficient future energy systems by enabling research on specific issues like grid planning and reinforcement while considering regional constraints, such as grid congestion or voltage violations. This supports a comprehensive understanding of low-voltage energy systems in the context of the energy transition.

Due to the inherent complexity arising from powertrain, aerodynamic, and unseen driving scenarios, efficient coordination of connected hybrid electric vehicles (C-HEVs) platooning encounters complex challenges. Existing studies primarily do not consider thermal load impacts. However, this overlooks the interconnection between the C-HEVs related to powertrain power and thermal loads, which can hinder effective optimization and collaboration across C-HEV platoon members. To fully address this gap, a multi-agent deep reinforcement learning (DRL)-based joint cooperative adaptive cruise control (CACC) with an integrated thermal and energy management (ITEM) framework is proposed. Additionally, to decrease the sample complexity and accelerate policy learning, the vehicle-level (string stability and time headway) and powertrain-level (energy consumption and health indicator) information are integrated into the reward function to design a safety-and physics-informed DRL framework. To meticulously assess the mutual impact of thermal load and management on the platooning optimization problem, air drag and airflow reductions related to inter-vehicle distance (IVD) are accounted for with a high-fidelity model provided by Autonomie. Numerical and hardware-in-the-loop (HIL) experiments demonstrate that the proposed CACC+ITEM algorithm outperforms the classic DRL version in stability, energy-saving, driving comfort, and battery longevity.

This paper presents a novel approach to intelligent log management, using large language models (LLMs) to detect and resolve anomalies in real-time. By extending the capabilities of a model like GPT for log analysis, we can enable the automatic detection of anomalies with actionable recommendations. The model was trained on a dataset of system logs with hyperparameters optimized for anomaly detection accuracy. The evaluation shows that the model successfully identified 85% of anomalies with a false positive rate of 5%, significantly outperforming traditional log analysis tools. Further, the model provides actionable recommendations in 90% of cases, which reduce the Mean Time to Recovery (MTTR) by 40%.

This paper investigates beamforming design with partial channel state information (CSI) feedback aimed at maximizing the energy efficiency (EE) of a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multiuser system. By leveraging the spatial reciprocity, which means that the path angle information (PAI) and power angular spectrum (PAS) of sparse paths are similar in the uplink and downlink channels, we can acquire the downlink PAI and PAS at the base station (BS) via uplink estimation. Subsequently, we select several dominant paths that contribute to EE maximization among all the cascaded paths. Consequently, only the small-scale fading coefficients (i.e., the normalized path gain information, NPGI) of these selected paths need to be fed back from the user equipments (UEs), thereby significantly reducing the feedback overhead. Moreover, we update the active BS beamformer and passive RIS beamformer by using the feedback of partial NPGI to further improve the EE. Numerical results demonstrate the superiority of our proposed algorithms over conventional schemes.

We discuss machine learning and its application to security, microchips and present a testable mathematical hypothesis on how the brain stores memories. In particular we present prototypes for next generation processors and RAM memories.

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.

Comparing data stored in HBase, a data warehousing platform for semi-structured data, and Solr, a search engine platform, presents a unique challenge due to the inherent differences between these two systems. This paper will explore the challenges of this comparison and propose effective strategies to overcome them. Performing this process manually can be time-consuming and labor-intensive. However, our simple Unix Shell Script automates the process, saving an estimated 15,000 soft dollars.

The aim of this research was to exactly quantify pure sodium metamizole from tablets , using a spectrophotometric analysis in Visible range. The method applied has been subjected to a validation protocal which consisted in analyzing the following parameters: linearity of the method, detection limit (LD) , quantitation limit (LQ). Following actual dosing, pure sodium metamizole amount in tablet of pharmaceutical was found to be 477.477 mg assigned to a percentage content of 95.495 %, very close to official declared amount (500 mg), with an maximum average percentage deviation of only 4.505 % from the official declared active substance content. This value was situated below the maximum admissible percentage deviation from stated active substance content (± 5%), established by Romanian Pharmacopoeia, X-th Edition rules.

Flexure bearings provide precise, low-maintenance operation but have a limited range of motion compared to conventional bearings. Here we introduce a new class of bearing – the metamorphic flexure bearing – that retains the advantages of precision, low wear, and low hysteresis over its limited flexure-bearing range, but provides an extended range of motion as needed. This extended range of motion is achieved via a position-activated transition to a conventional sliding or rolling bearing. To demonstrate the operating principles of this new class of bearing, we describe, design, assemble, and test a linear-motion metamorphic flexure bearing using three categorically-different transition mechanisms: a compression spring, a constant-force spring, and a pair of magnetic catches. This design paradigm has the potential to provide various benefits, such as reduced wear, reduced downtime, cost savings, and increased safety, in areas ranging from precision manufacturing to healthcare robotics to biomedical implants.

Image reconstruction is the manipulation of digitized information obtained during body imaging into interpretable pictures that represent anatomical details and diseases. It is a vital step in producing visual results from a Computed Tomography (CT), Magnetic Image Resonance (MRI) etc. scanning devices. In all modalities (CT, MRI etc.) the data is acquired in form of slices or projections. In simple terms, the process of putting the projections together in a two-dimensional (2D) or three dimensional (3D) scans can be called image reconstruction. Back projection and forward projections are two commonly used operations in the image reconstruction pipeline. And since the image reconstruction algorithms can be compute intensive, there is desire to fasten the processing time for it, to get the final scan faster. The scans are what a medical professional such as a radiologist use to provide diagnosis to a patient, hence faster access to scan can result in more timely diagnosis.  With the goal to reduce the processing time for image reconstruction pipeline, the authors worked to analyze and optimize representative back projection and forward projection algorithms. This paper provides details on the various optimization and code migration work that was done to achieve targeted performance metrics using a hardware vendor neutral programming language.

In the field of information technology, cybersecurity is essential and one of the main concerns of today is information security. Ransomware is one of the more concerning cyber threats that is always evolving. This type of cyberattack entails encrypting the data or files belonging to the victim and requesting payment for the decryption process. Ransomware occurrences continue to increase in frequency, inflicting substantial disruption and financial loss despite the various cybersecurity precautions implemented by governments and organizations. This study does a thorough analysis of the literature on the creation and application of ransomware exploits. It looks at the techniques used to make ransomware, the weaknesses that are exploited, and how ransomware strategies have changed over time. This paper attempts to highlight areas for further research and uncover common trends in the evolution of ransomware exploits by synthesizing findings from multiple investigations. The assessment also covers defense tactics, new difficulties in battling this constantly changing danger, and the life cycle of ransomware attacks. This thorough investigation aims to advance our knowledge of ransomware exploit techniques and support the creation of stronger cybersecurity defenses.

A document by Fan Gao. Click on the document to view its contents.

Today, heart disease is one of the most common causes of death in the world. For this reason, accurate and timely diagnosis of patients' conditions is essential. Because traditional diagnostic methods for these kinds of diseases have many costly side effects, researchers are always looking for cheaper and more accurate ways to diagnose. One effective strategy is using mobile health devices to monitor the person's health status through the signals received by an electrocardiogram. Early diagnosis enables more effective treatment and prevention of chronic diseases. Measuring and recording electrocardiogram signals by mobile services is a valuable solution in the field of health care, such as processes for classifying and diagnosing the activity or condition of patients. This study aimed to find a model with a precise function for classifying and recognizing human activities using cardiac data taken from wearable sensor signals from two electrocardiography sensor. This classification model of human activity is based on the electrocardiogram data of the m-health data set and obtained using four deep learning models. These models include recurrent neural networks like (LSTM), Convolutional Neural Network (CNN), and attention-based hybrid neural networks. This research has taken a step in classifying different types of human activities only through electrocardiogram signals. By examining other hyper parameters and selecting the best ones, our proposed network has achieved slightly higher accuracy than previous research. The proposed CNN-LSTM combination-based learning model obtained approximately values of 100% accuracy, 100% precision, 100% recall, 100% F1 score, and 100% for the ROC AUC scale, respectively, in terms of validation results. The results show that our designed network performs better than the advanced models of previous research.