A document by Qi Shen . Click on the document to view its contents.

A document by Bozhen Jiang. Click on the document to view its contents.

With the exponential growth of the Internet of Things (IoT) landscape and the resulting spectrum congestion, innovative techniques for spectrum monitoring are crucial. This paper presents a groundbreaking approach to spectrum monitoring harnessing the power of spiking neural networks (SNNs) with a focus on image segmentation using the UNet architecture. Traditional methods, including energy detection, have been widely used but are not without challenges, especially in environments with varying signal-to-noise ratios. In contrast, the presented SNN approach in this paper leverages the leaky integrate-and-fire neuron model and provides superior energy efficiency, real-time inference capability, and higher detection performance. Through extensive simulations, the proposed SNN framework exhibited performance metrics that significantly surpass energy detection methods and closely align with conventional convolutional neural network techniques. Future explorations will delve into enhancing the framework using machine learning techniques for advanced feature extraction and multi-class segmentation.

A document by Gerald Lerner . Click on the document to view its contents.

Interferometric Synthetic Aperture Radar (InSAR) time series analysis is a powerful technique to estimate long-term water level changes in wetlands ecosystems. However, few studies have applied InSAR on wetlands that are highly segmented by canals and levees due in part to the challenge of selecting qualified reference points to minimize unwrapping errors, which, by contrast, is a relatively easy task for unsegmented wetlands. Here we developed a new method to automatically select the optimal reference point for InSAR time series analysis. The method selects reference points by considering temporal behaviors of coherence and InSAR phase connectivity from each reference point to its wetland of interest. We tested the method on six managed and highly segmented wetland units within the Sacramento National Wildlife Refuge in the Central Valley, California. We validated the InSAR measurement against water depth gauge measurements during a low water depth (< 0.05) for five out of six units and similar RMSE for the remaining one. This new automatic method enables us to maximize the performance of InSAR to predict water depth and could be applied to other types of InSAR applications as well.

The multi-tenant resource-sharing environment of cloud computing among multiple users and vulnerabilities of virtual machines (VM)s and physical machines prompt unauthorized access to users’ confidential  data and excess power consumption and resource wastage. This paper presents a comparative survey study of securing cloud resource strategies to address this critical issue. Specifically, this study focuses on Defensive Strategies associated with cloud resource allocation with their pandect comparative summary.

Detailed design requirements and foundational assumptions for forecasting digital twins in the context of power systems are explored and used in real-time forecasting in this study. Experimental validation of the forecasting methodology is demonstrated via an electro-thermal digital twin of power distribution cables for onboard power systems. The digital twin has the capability to forecast the thermal profile of cables by utilizing sensor measurements from the physical twin. When the predicted temperature reaches specific thresholds, the digital twin informs a decision maker to proactively adjust the power flow within the system to prepare for and avoid upcoming thermal constraints in the cable. This adjustment ensures that the physical cable does not reach specific thermal constraints, thereby enhancing system reliability. This proactive management will be essential to ensuring mission-critical power demand and avoiding load shedding. The concept has been experimentally verified using a three-bus configuration. The developed digital twin is computationally efficient, forecasting only when necessary, and offering an adjustable forecasting time-frame to accommodate a variety of operational scenarios.

This paper compares different types of GAA FET structures at 3 nm technology node using TCAD simulation, including Lateral Nanosheet, Lateral Nanowire, Vertical Nanosheet, and Vertical Nanowire. Single gate and gate stack structures are included in the analysis. The performance is evaluated using the key device parameters such as on-state current, leakage-current, thresh-old voltage for transfer characteristics, drain-induced barrier lowering (DIBL) and subthreshold slope (SS) for short-channel effects. Among the four GAA structures we found that, under the same effective width, Lateral Nanowire structure shows the highest on-state current, but also the worst performance in short channel effects. In contrast, Vertical Nanosheet shows the lowest DIBL and SS, thanks to

A full-size converter-fed synchronous machine (CFSM) for variable speed operation of a pumped storage power plant exhibits multiple advantages over the state-of-the-art doubly fed induction machine (DFIM) technology. The CFSM technology is emerging as the most flexible system for pumped storage plants for efficient operation in a wide range of water flows, which is not the case in existing power plants.  This article presents steady-state control strategies to execute the variable speed operation of the pumped storage power plants in both turbine and pump mode using a full-size back-to-back converter. Furthermore, the starting method of the power plant in pump and turbine mode from a standstill is proposed. In addition, the seamless transition of operating modes from the turbine to pump mode and vice versa is presented, which is an essential feature required to adapt to the varying power generation from renewable energy sources like solar and wind. The behavior of the power plant during a voltage dip on ac grid, and the low-voltage ride-through (LVRT) capability in both turbine and pump modes are also presented. The proposed control strategies are experimentally verified with a 100 kVA machine and converter setup with an emulated reversible pump turbine (RPT) and waterways.

A document by Hung Nguyen-Kha . Click on the document to view its contents.

Ocular image processing is an important step in applications like face recognition, driver fatigue detection, iris recognition, and gaze tracking etc. Secondary reflections and glare formed on the spectacles results in poor detection and localization of ocular features. Separating the reflection components accurately is a relevant and important challenge in enhancing eye feature quality under spectacles. Direct application of Dichromatic reflection model (DRM) based reflection removal algorithms on real-time facial images under spectacles—results in loss of ocular feature information in the diffuse component—as the DRM approaches fails to separate the chromatic and achromatic part of diffuse reflection. In this manuscript, we solve the problem of hue-saturation ambiguity by extending the DRM to Hue-Saturation-Value (HSV) colour space, and propose a new single image-based spectacle problem removal approach. Experiments on three set of databases demonstrate that the proposed approach achieves better solution with minimum execution time compared to the state-of-the-art.

This paper presents a comprehensive approach to designing and optimizing a Hierarchical Rule-Base Reduction (HRBR) based Adaptive-Network-Based Fuzzy Inference System (ANFIS) for symmetric linguistic variables. Specifically, the linguistic joint membership functions that underlie the ANFIS are defined, focusing on symmetrical inputs/outputs and jointly optimized trapezoid membership functions to reduce the number of training parameters. Further optimizations for the ANFIS were derived based on design assumptions, including training the membership functions on closed or single-sided domains. The optimal output membership weights based on mean square error optimization were also symbolically obtained. The online training of the ANFIS’s input/output membership functions was performed using the DDPG (Deep Deterministic Policy Gradient) algorithm. A simulated skid-steered vehicle was used to validate the approach and performed waypoint-to-waypoint path following. Experimental results using the Clearpath Jackal demonstrated that the ANFIS model converged quickly, typically within 6 to 10 episodes of training, from an initial MAE and RMSE of 0.88 and 1.02 meters, respectively, to a final MAE and RMSE of 0.087 and 0.10 meters. The results highlight the effectiveness of the ANFIS approach for vehicular robotics applications and suggest promising avenues for future research and development.

The quadrotor is an under-actuated nonlinear system that poses challenges in modeling and controller design. This work uses the translational (Newtonian) and rotational (Eulerian) dynamics of the quadrotor to build a decoupled control system. A linear quadratic Gaussian (LQG) regulator is used as the controller, and two extended Kalman filters are used for state estimation in the two dynamic models. The entire design process from modeling to flight simulation is presented. Model linearization while preserving controllability, state-space modeling under gravity disturbance, feedforward compensation, LQR gain adjustment, actuator dynamics, sensor system for observability, adaptation of extended Kalman filter, and its limitations are presented.

This paper explores the pivotal role of open source software (OSS) in advancing software engineering technologies and catalyzing their adoption at scale within industries. We analyze how the OSS model promotes rapid innovation, transparency, collaboration, and economic efficiency, and examine real-life case studies of industrial adoption. We underscore the role of OSS in bridging the gap between research and practice, aiding in the translation of research findings into real-world applications, fostering academia-industry collaboration, and promoting continuous learning and skill development. Furthermore, we discuss the future prospects of OSS in software engineering, touching upon its role in emerging technologies, its governance, its potential in enhancing security, and its contribution to sustainability. Our analysis illustrates that OSS has played an integral part in reducing the divide between research and practice, accelerating the journey from idea to impact, and shaping the trajectory of software engineering and industrial landscapes.

Detecting personal identifiable information (PII), personal healthcare information(PHI) and similar sensitive data within structured datasets is imperative for various reasons. This has become an important problem for organizations to solve automated detection especially amidst high volumes of data collection with the rise of data protection laws and user privacy importance. Multiple sensitive data detection methods exist in structured and unstructured datasets. Structured data pose a greater challenge for such problems due to lack of external context and general understanding of the data. We describe the importance of the problem's existence. We list out existing solutions in the space with their limitations and direct towards the solution using generative artificial intelligence. We want to focus on how the latest advancements in LLMs can provide a truly global solution to the global problem such as sensitive data detection.

This paper proposes a pre-processing method for the assimilation of microwave satellite observations over winter sea ice into a numerical weather prediction system.

Nonlinear signal distortion, caused by passive intermodulation (PIM), presents a major challenge to modern multi-carrier communications and radar systems. Reverse PIM products generated by discrete nonlinear sources are analyzed in this paper, and the means of PIM mitigation are proposed. The developed model is validated experimentally using an open-ended coaxial waveguide with a movable iron insert, which acts as a main PIM source. The PIM generation mechanisms revealed in this study have identified alternative means for PIM mitigation. A novel circuit, proposed in the paper, enables an effective reduction ofthe reverse PIM for more than 44 dB.

A document by Praditha Alwis . Click on the document to view its contents.