To extend the flight duration of unmanned aerial vehicles (UAV), laser-powering systems have been proposed in recent times that can charge a flying UAV with laser beams from the ground. In this study, we analyze the potential of using a ground-based laser station to charge a fixed-wing UAV flying in a turbulent atmosphere. This presents a new design challenge in optimizing the UAV's trajectory by jointly considering atmospheric turbulence and UAV energy consumption. To overcome this, we derive theoretical models which capture the UAV velocity and position evolution along a circular trajectory in a turbulent atmosphere. Based on the derived models, we first compute the received energy loss at the UAV that depends on the UAV velocity (variance) as well as the trajectory radius. Then, based on the received energy loss and the energy consumption models of the UAV, the UAV trajectory is optimized with respect to velocity and trajectory radius to maximize the net laser power harvested by the UAV. Our results show that the optimal velocity and trajectory radius depend strongly on the levels of turbulence in the atmosphere.

\cite{p2023}Human Activity Recognition (HAR) is a field that automatically recognizes and categorizes human activities based on data collected from wearables, smart homes, and other IoT devices. Recognizing human activity is not an easy task because an action can occur periodically, and two activities can have almost similar properties in terms of signals received from sensors. Also, how an activity is performed may differ from person to person. This study describes how to identify a specific type of human physical activity using human pose detection. Human pose detection involves detecting critical key points that can describe the orientation or movement of the human body by correlating them with critical points of the body to detect body parts of interest. Therefore, proposing a method to label and mark activity transitions between two consecutive activities using body key detection and machine learning classifiers as the fundamental human activity recognition in the learning and classification module can improve the prediction probability of Human Activity Recognition. The combination of these two techniques improves the performance of the Human Activity Recognition system, making it more accurate in recognizing complex and varied human activities.

The importance of a measurement of power system inertia in the context of the continuing replacement of synchronous generators with renewable energy sources is considered. A detailed description of the frequency time-series associated with the power system response to the loss of a major source of power in-feed is discussed. The purpose of this is to identify what part of the response is operationally associated with inertia, such that it can be measured and subsequently used for grid control and planning. A novel reference method is proposed, in particular, a signal processing transform to decompose the frequency time-series in order to isolate the operational inertial response. Power system modeling of a major loss event on the GB grid is used to verify the method. A measurement campaign is described and measurements from 23 major infeed loss events in the GB grid are presented. Inertia results from the new method and associated uncertainties are compared with the electricity system operator's estimates for each loss event.

In this letter, to handle the over-generation issue of photovoltaic (PV) systems based on maximal power point tracking (MPPT) algorithms, and to provide the power reservation for power grids, an optimal point shifting design based on virtual impedance is proposed for PV systems with flexible power point tracking. With the introduction of the virtual impedance, the virtual PV power is provided to auto-shift optimal power points on PV power-voltage curves. With a MPPT algorithm applied to the virtual PV power, the actual PV power is auto-turned without the estimation of maximum available PV power. The proposed design is validated through simulation studies of a PV-battery charging system and experimental studies of a grid-tied PV system, where both the overcharging of the battery and the over PV power generation are effectively avoided by reducing actual PV power according to the increasing of the battery SOC or the grid RMS voltage, respectively.

In this paper, an optimized hardware architecture is presented for a motor imagery-based Brain-Computer Interface (MI-BCI) tailored for real-time, wearable applications that demand high accuracy and low power consumption. The design captures and filters EEG signals to isolate motor imagery patterns, utilizing Linear Discriminant Analysis (LDA) for feature extraction followed by Euclidean distance-based classification. Key optimizations, including sequential matrix multiplication, pipelining, and an FSM-based control unit, achieve 91.67% accuracy with a power consumption of 2 mW on 180 nm technology, significantly enhancing performance compared to conventional designs.

Reconstruction or imaging of objects, as one type of electromagnetic inverse problem, plays a crucial role in applications such as medical imaging, non-destructive or noninvasive detection and sensing. This paper presents a method for electromagnetic imaging of shapes of scattering objects. Specifically, we employ a metallic enclosure, apply a timereversal imaging method (TRIM) and leverage the equivalent source principle to reconstruct the shapes of the objects (or scatterers). The preliminary results verify the efficacy of the proposed TRIM, demonstrating its ability to solve inverse problems without resorting to intricate mathematical formulations or Green's function. Thus, it offers a practical and efficient alternative solution to inverse imaging problems.

Short-term instability is one of the key challenges for redox-based resistive random-access memory (ReRAM) reaching practical applications in the field of neuromorphic computing. Developing programming schemes that balance resistance fine-tuning with short-term stability is crucial for improving the reliability of ReRAM devices. In this work, we explore the impact of programming schemes on the short-term variability of valence change mechanism (VCM)-based filamentary ReRAM devices. We design three programming schemes: a baseline multi-pulse scheme, a single short-pulse scheme, and a single long-pulse scheme. By comparing the programming statistics of the one-transistor-one-resistor (1T1R) structures under different schemes and analyzing their evolution over time, we propose the hypothesis that the number and width of pulses influence the filament size and internal distribution of oxygen vacancies, and thus the short-term stability of ReRAM devices. We employ three-dimensional Kinetic Monte Carlo simulation, constructing filaments of varying sizes and adjusting the oxygen vacancy distribution to simulate the outcomes of different programming schemes. The simulation results align with experimental findings. Based on existing experimental data and modeling studies, we propose programming strategies that balance programmability and reliability for VCM-based filamentary ReRAM devices.

The absence of experimentation and analysis regarding the mechanics of the Diaphragm in the active state or the tetanized state of the Diaphragm muscle signals for a need of research to account and simulate the mechanics of this particular state. To simulate a basic structure of the diaphragm in a certain period, mathematical equations outlining the shape of the diaphragm generated by Prof. Yi-Chao Chen from the University of Houston from previous experimentations was utilized in the simulation code. Consequently, a video was created that provided a base simulation of the mechanics and motion of the diaphragm during its expansion and contraction phases. This study and any associated code serves as a foundation for the advancement of the collective research of our lab group, with the lead investigator being Prof. Yi-Chao Chen and as a gateway for future research on the mechanics and mathematics associated with the Diaphragm motion in the active state.

This paper aims to introduce new method for forcing logical thinking to AI by delicate instruction prompting with multiple executions. exhaustive prompting uses general reasoning formation to force the way of thinking to the AI by accomodating of essential factors in prompting. With this new methodology, accuracy in well-known benchmarks has been boosted up at 1.0%p to 9.7%p.

This study presents a novel method for visualizing the time-dependent development of a scientific discipline using UMAP (Uniform Manifold Approximation and Projection) and word embeddings. By encoding the abstracts of scholarly articles into a high-dimensional space and then projecting them into a 3D space, we demonstrate how the evolution of research interests and topics in a specific field can be mapped over time. This approach o↵ers new insights into the dynamics of scholarly discourse and the emergence and disappearance of research themes.

The task of knowledge graph completion poses significant challenges due to the vast scale and complexity of relationships that need to be predicted within large datasets. Introducing a novel concept of partial dormancy within the GPT-Neo architecture offers a significant advancement by enabling selective neuron activation, thereby reducing computational overhead while maintaining high accuracy. Through dynamic gating mechanisms, the model efficiently handles diverse input complexities, resulting in optimized performance across various knowledge graph sizes. The model's ability to balance computational efficiency with high accuracy highlights its potential for deployment in resource-constrained environments. Experimental results demonstrated that the partially dormant GPT-Neo outperforms fully active models in both efficiency and scalability, suggesting a transformative approach for future applications in knowledge graph-related tasks. The contributions made extend beyond enhancing model efficiency, providing a foundational framework that may influence the development of more adaptable and energy-efficient language models.

Topology inference of non-collaborative wireless network has been extensively investigated for network science and military reconnaissance field. Instead of using linear Granger causality (GC), this letter proposes a novel Difference Neural Granger causality (DNGC) architecture for learning network topology from sampled time series without accessing protocol. With leveraging the hierarchical penalty and differencing approach as an adaptive weight, the proposed DNGC could capture the dynamic and nonlinear connections between neighboring time steps in the collected sequence. Extensive simulations show that the proposed DNGC outperforms the existing GC approaches, especially when the observation time is limited.

This study proposes and evaluates a novel implantable antenna testing method using a tissue gel phantom combined with a living human subject to simulate realistic implantation conditions. The gel phantom, shaped by 3D-printed molds and composed of water, sugar, salt, and agar, enables antenna performance analysis through channel gain and Received Signal Strength Indicator (RSSI) measurements in mobile environments. By incorporating human subjects, we capture channel gain variations in dynamic conditions influenced by design, gain, implantation site, and subject movement. This novel setup allowing rotational movement and untethered Bluetooth Low Energy (BLE) packet analysis reveals a previously undetectable 20 dB difference between line-of-sight and non-line-of-sight scenarios, highlighting the limitations of traditional bench-top phantoms.

With the rise of Industry 4.0 and intelligent manufacturing, real-time anomaly detection and predictive maintenance have become critical components to ensure operational efficiency and product quality. This paper explores convolutional neural networks (CNNs) for predictive maintenance within the context of a milling process. We developed and trained a CNN model to predict tool wear under varying operational conditions using the Milling Data Set from UC Berkeley’s BEST Lab, which captures sensor data such as vibration and acoustic emission during milling operations. The performance of the CNN model was evaluated in terms of Mean Squared Error (MSE) and R 2 , demonstrating its capability to detect wear patterns and predict potential tool failures with an MSE of 0.056. The results were benchmarked against traditional machine learning models, such as Random Forest, Linear Regression, and XGBoost. Insights into feature importance were gained by analyzing the contributions of vibration and acoustic emission data to tool wear prediction. These findings suggest CNNs, when optimized for sensor data, can be effectively integrated into smart manufacturing systems, offering real-time predictions to reduce machine downtime and extend tool life.

This study presents a novel framework for self-supervised motif discovery in long temporal data, an approach focusing on enhancing explainability and identifying intrinsic differences between motifs. Our research fills a critical gap in motif analysis by introducing a three-pronged methodology: extracting motifs, comparing their signatures, identifying them, and interpreting their differences. The proposed self-supervised technique is designed to ascertain the expected number of motifs in a dataset and to extract them effectively. This approach is reinforced by a robust algorithm that identifies unique motifs and their signatures and a proper distance metric for comparing partially similar motifs. This metric is crucial in determining the similarity between motifs of varying lengths or those containing noise. The application of our framework to ECG data demonstrates its effectiveness in distinguishing between normal and irregular heartbeat patterns without supervision. This capability is a significant advancement in motif analysis, offering a scalable and noise-resistant solution that simplifies the process of segmenting, identifying, and understanding complex temporal patterns. Our results of over 99.9% accuracy underscore our approach's potential in a wide range of applications, particularly in healthcare diagnostics, where accurate and explainable motif analysis is paramount.

Most existing graph keyword search works assume that the graph data is complete and clean, that is, there are no missing information (such as keywords or edges) and contaminated information (such as keywords) on the graph. However, real-world graphs often suffer from missing or being contaminated, making the keyword search on graphs much more challenging. To solve the problem of keyword search on incomplete or contaminated graphs, we propose a transformation paradigm to convert the keyword search on the incomplete or contaminated graph into the approximate keyword query in an embedding space. Based on this paradigm, we designed a specific framework named DKS. DKS can simultaneously answer the keyword search on incomplete or contaminated graphs within polynomial time and space complexity. In offline, DKS first adopts a GNN-based model to clean the contaminated graph or complete the incomplete graph. And then, by capturing neighborhood keyword distribution and graph structure for each node on the graph, DKS obtains the representation of each node in the embedding space. In online, answers for the keyword search are obtained based on these node representations. We conduct experiments on 5 real-world datasets to evaluate the effectiveness and efficiency of DKS. The experimental results show that DKS can improve the accuracy by up to 2-5 times on contaminated graphs and 1-2.8 times on incomplete graphs compared to the state-of-the-art methods.

The development of advanced detection frameworks has become critical in addressing the escalating sophistication of cyber threats targeting data security and operational stability. Hierarchical Threat Signal Profiling (HTSP) introduces a novel approach that integrates autonomous profiling mechanisms with hierarchical modeling techniques to enhance threat detection capabilities. The framework achieves precise identification of malicious activities through layered analysis of signal behaviors, effectively addressing limitations in traditional signature-based and heuristic-based methods. Detection performance was rigorously evaluated across multiple ransomware families, demonstrating high accuracy and low false positive rates in diverse testing scenarios. Behavioral profiling within the HTSP framework leverages multi-dimensional analysis to distinguish unique ransomware patterns, offering actionable insights into malicious operations. Modular system architecture ensures scalability and compatibility with existing cybersecurity infrastructures, while maintaining efficiency in high-throughput environments. Resource monitoring revealed the potential of CPU utilization and encryption speeds as auxiliary indicators for real-time detection. The framework also excels in identifying anomalies in network traffic and file access patterns, further strengthening its operational reliability. Advanced techniques, such as dynamic profiling and predictive analytics, enhance the framework's adaptability to emerging attack strategies, ensuring comprehensive protection against evolving threats. Experimental results affirm its capacity to handle diverse challenges, positioning the HTSP framework as a groundbreaking solution for securing critical digital assets. The research demonstrates the importance of autonomous systems in modern cybersecurity paradigms and sets a strong foundation for continued innovation in proactive threat mitigation.

This work evaluates the compression techniques on ConvNeXt models in image classification tasks using the CIFAR-10 dataset. Structured pruning, unstructured pruning, and dynamic quantization methods are evaluated to reduce model size and computational complexity while maintaining accuracy. The experiments, conducted on cloud-based platforms and edge device, assess the performance of these techniques. Results show significant reductions in model size, with up to 75% reduction achieved using structured pruning techniques. Additionally, dynamic quantization achieves a reduction of up to 95% in the number of parameters. Fine-tuned models exhibit improved compression performance, indicating the benefits of pre-training in conjunction with compression techniques. Unstructured pruning methods reveal trends in accuracy and compression, with limited reductions in computational complexity. The combination of OTOV3 pruning and dynamic quantization further enhances compression performance, resulting 89.7% reduction in size, 95% reduction with number of parameters and MACs, and 3.8% increase with accuracy. The deployment of the final compressed model on edge device demonstrates high accuracy 92.5% and low inference time 20ms, validating the effectiveness of compression techniques for real-world edge computing applications.