Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiresolution Analysis-Inspired Spatial and Spectral Details Preserved Model for Variational Pansharpening

Pansharpening, which is also known as the fusion of low-resolution multispectral (LRMS) and panchromatic (PAN) images, refers to producing a high-resolution multispectral (HRMS) image by preserving the spectral detail from the LRMS image while extracting the spatial detail from the PAN image. In this article, we revisit and novelly reinterpret the multiresolution analysis (MRA)-based pansharpening framework as the fusion framework of “spectral detail + spatial detail” and can obtain two alternative formulations of spectral detail and spatial detail, respectively, and hence propose a novel variational pansharpening method with MRA-inspired spatial and spectral details preserved model. First, the spatial degradation relationship between HRMS and LRMS is imposed as the spectral fidelity term. Second, based on the new reinterpretation of “spectral detail + spatial detail” of the MRA fusion framework, we propose to use the structure tensor to model the spatial detail image and propose a new structure tensor total variation (STV)-guided spatial detail preserved prior term. Moreover, to model the spectral detail image, we propose to impose the spectral detail preserved constraint between the two alternative formulations of spectral detail as the MRA-inspired spectral detail preserved prior term. Then, we optimize the proposed model via the alternating direction method of multipliers (ADMMs). Furthermore, various experimental results on the reduced-scale and full-scale datasets validate the superiority of the proposed method.
Increasing Sensitivity of Magnetic Tactile Sensors by Optimizing Arrangement of PM Array

This study focuses on enhancing the sensitivity of magnetic tactile sensors by rearrangement of the magnetic circuit using a specialized permanent magnet structure, specifically the implementation of a Halbach array configuration strategically placing the permanent magnets to optimize the horizontal magnetic flux density of the sensing element, while concurrently minimizing interference from side magnetic circuits. By adopting this approach supported by finite element analyses, the sensitivity of tactile magnetic sensors is found to be significantly amplified, leading to enhanced accuracy and performance in various applications.
Bio-Inspired Gesture Recognition with Baffled Transducers Using Temporal and Spectral Features

Echolocating bats can provide engineers with tremendous inspiration for constructing active ultrasonic sensors for airborne applications. Previous research has demonstrated that the recognition of hand gestures can be facilitated by means of ultrasonic sensing, often relying on classical engineering principles. In this paper we merge the insights from research into biological echolocation systems with gesture recognition, and present a gesture recognition sensor inspired by the echolocation system of bats, using spatiospectral features induced by the geometric shape of baffled microphones. We use the spatiospectral features in combination with a support vector machine classifier as gesture classification mechanism. We show the efficacy of the proposed approach using experimental data gathered from twenty persons performing four different gestures.
Dark Activity Detection in AIS-Based Maritime Networks

The Automated Identification System (AIS) is an indispensable tracking system employed in the maritime industry for vessel identification, location tracking, and collision avoidance. While AIS messages provide essential information for maritime traffic management, they also present challenges when vessels aim to conduct operations discreetly or evade observation. This phenomenon, referred to as “dark activity”, involves intentional AIS deactivation by vessel operators seeking to conceal their actions, often related to illicit or illegal maritime activities such as smuggling, piracy or illegal fishing. The detection and monitoring of dark activities pose significant challenges for law enforcement and security agencies. This paper explores innovative approaches to address this issue by harnessing AIS data and incorporating rule-based techniques, as well as machine learning techniques to enhance maritime security efforts. We adopted a local approach where a dark activity of a vessel is detected by nearby ships depending on the previous signals. We implemented a detailed simulation environment based on real and realistic data to run the proposed algorithms. Simulation results show that while rule-based approach is successful in detecting dark activities, it tends to produce false alarms, and ML-based approach provides better overall accuracy.
5G E2E Network Slicing Predictable Traffic Generator

Automated resource management for 5G network slicing implies the need to assign each slice the necessary resources, i.e., the ability to predict their respective requests and resource requirements. Machine learning models and algorithms can meet these needs provided the required data is available. Unfortunately, 5G traffic data remains sparse despite many studies relying on machine learning models and algorithms for traffic forecasting or automated network resource management. In this study, we introduce a 5G-type predictable traffic generator that relies on the refactoring of open data of vehicle and pedestrian traffic from the City of Montreal. Indeed, the latter data is refactored in order to generate different classes of network traffic, with different characteristics associated with typical 5G applications, and then with different traffic patterns and peak hours. The result is a valuable traffic generation tool for researchers interested in validating machine learning algorithms aimed at, for example, traffic forecasting, resource elasticity, or automated scaling of slice resources.
Do Foreign Direct Investment and Innovation Influence ICT Goods Imports in ASEAN 6?

Current technological developments have changed people’s lifestyles to become more modern. Technological developments encourage faster digitization in the world. When a country cannot meet its own domestic demand for digital products that involve advanced technology, it tends to rely on importing high-technology products, specifically those in the field of Information and Communication Technology (ICT). The aim of this study is to see how FDI and innovation influence the ICT goods imports in ASEAN 6. The analytical method used in this study is the Generalized Method of Moment panel data analysis of ASEAN’s 6 countries, namely Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam, for 21 years from 2000 to 2020. Based on the results of the System GMM regression estimation, it was found that FDI had a positive and significant impact on ICT goods imports in most of the models. In comparison, innovation has a negative but not significant impact on ICT goods imports. Foreign investment has the potential to encourage imports of ICT products. In the short term, ICT goods imports are a necessity due to the absence of substitute products that have the same specifications and quality as imported products. In some cases, innovations may be in their infancy or not mature enough to significantly influence import patterns. Therefore, the government needs to consider long-term programs related to the development of industries capable of producing ICT goods. After the domestic industry has the capacity to produce ICT products, in the next stage, the government can consider policies on local content requirements to suppress imports, especially ICT goods.
Interannual Variability in Net Ecosystem CO~2 Exchange at the Arctic Treeline

Net ecosystem exchange (NEE) of carbon dioxide was measured at treeline forest and fen tundra sites near Churchill, Canada during three consecutive growing seasons (1997-1999). Although both sites demonstrated a net uptake of CO2 in each of the years, there was considerable variation in the flux between sites and between years. Mean daily NEE at the forest varied from -1.5 (+0.25 SD) g CO2 m-2 d-1 in 1997 to -7.3 (+0.39) g CO2 m-2 d-l in 1999. The fen mean daily NEE varied from -1.1 (_0.36) g CO2 m-2 d-1 in 1997 to -3.1 (+0.50) g CO2 m-2 d-1 in 1998. Integrated over the 65-d measurement period the forest was a net sink of -100, -313, and -478 g CO2 m-2 in 1997, 1998, and 1999, respectively, and the fen net sink was -73, -202, and -38 g CO2 m-2, respectively. Interannual variations in the fen NEE were closely related to water table drawdown (soil wetness), the forest NEE was unaffected by water table change. The forest seasonally cumulative NEE was closely associated with timing of snowmelt and accumulated heat content prior to leaf-out. Earlier snowmelt and greater heat accumulation produced a larger growing season sink. It is likely these events also influenced the fen NEE, but the relationship is complicated by water table effects. These results suggest that carbon dioxide exchange varies considerably across the northern treeline, and that there may be an important biospheric feedback between climate warming, treeline advance, and carbon cycling.
Monte Carlo-Based Transmission and Subtransmission Recovery Simulation of Hurricanes

High-impact low-probability (HILP) events can wreak havoc on electric power systems without appropriate preparedness. In this paper, the recent development of a tool, named Recovery Simulator and Analysis (RSA), is described and demonstrated. While there are many issues to consider when recovering electric power systems, the focus of RSA is on the coordination of transmission and subtransmission recovery with generation dispatch to minimize unserved energy. RSA focuses on recovery simulation to evaluate resilience as part of a planning process. RSA is demonstrated on an approximately 1400-bus Puerto Rican power system for 100 simulated instances of Hurricane Maria. Analysis of the recovery determines how many lines are critical to the recovery and which loads may experience delayed recovery. The results demonstrate the potential uses of RSA for identifying recovery decisions with low unserved energy and for identifying assets critical to recovery which can then be hardened prior to a HILP event.
A Multi-Port Converter: Efficiency and Output Power Analysis

The current research project focuses on improving and optimising the operation efficiency of a multi-port converter with 1.55 kW of output power. A multi-port coupling, representing the heart of an electronic transformer, is described. The main details and parameters that determine its behaviour and their balance equations are summarised. An extensive investigation has been conducted to assess the system's effectiveness and power supply capacity for each port across various scenarios. The primary objective is to assess both the overall effectiveness of the converter and its performance in each case study. In this context, the study presents compelling diagrams and tables that showcase the remarkable improvements in energy efficiency and power achieved by this small-scale multi-port converter. The conclusions drawn from this work emphasise the positive impact of this optimisation on energy efficiency, potentially leading to significant advancements in the application of such devices on a smaller scale.
HFR Video-Based Hornet Detection Approach Using Wing-Beat Frequency Analysis

This paper proposes a novel approach to effectively detect and distinguish honeybees and hornets by analyzing their wing-beat frequency based on high-frame-rate videos. First, we extract foreground objects that possibly contain insects by using the K-nearest neighbor algorithm. Hungarian algorithm is applied to calculate the correspondence relation between centroids from different video frames. Thereafter, we calculate the centroid displacement of these foreground contours and obtain their displacement signals. We then filter these signals through the empirical mode decomposition method to exclude the global movement components. Finally, we examined the filtered signals using Fast Fourier Transform to identify the characteristic flapping frequency of different bee species. We conducted several field experiments to evaluate our method. The result proves the feasibility of the proposed method.
Deep Learning-Based Droplet Menisci Recognition for Digital Microfluidic Devices

This paper presents the application of machine learning techniques, specifically deep learning, to enhance the performance and reliability of electrowetting-on-dielectric (EWOD) digital microfluidic (DMF) devices. During droplet manipulation within an EWOD DMF device, the menisci of the droplets carry valuable information, including fluid properties, droplet size, position, and dynamics. Recognizing and analyzing these droplet menisci can provide insights into reaction kinetics and device defects, enabling reliable feedback control for improved device operation. To achieve reliable control of EWOD DMF devices, this study focuses on the recognition of droplet menisci using a deep learning approach. The U-Net model, a highly effective deep learning architecture for image segmentation tasks, is integrated into the present study. A convolutional neural network with the U-Net architecture is implemented, and image segmentation is performed to accurately identify droplet menisci. The network is trained using a diverse dataset containing images of droplet under various conditions. Model parameters are optimized to ensure high accuracy and robust performance. Experimental tests are conducted on images of droplets in different conditions, and the trained network successfully recognizes menisci with a model accuracy of 98 %.
Coupling QoS Co-Simulation with Online Adaptive Arrival Forecasting

Coupled simulation, also known as co-simulation, has been proposed to provide more information to a task scheduler by simulating at runtime the Quality of Service (QoS) arising from a scheduling action. To do so, co-simulation algorithms run the simulation assuming a static set of arrival time series, restricting the diversity of the traffic scenarios. To ensure the co-simulator can provide valuable and representative results, we present an online adaptive arrival forecasting framework that contains a change-point detection module and a probabilistic transformer model to couple co-simulators with arrival series forecasting. The framework can also update the prediction model to adapt to dynamic environments. Our experiments show that our online adaptive forecasting framework has lower forecasting errors than established prediction models, such as autoregressive processes, and lower on real-world traces the co-simulator prediction error by up to 27 % on average response time and 39% on average service-level agreement (SLA) violation.
An Extended Single-Valued Neutrosophic EDAS Model for Multi-Attribute Decision-Making and Its Application to Innovation Capability Evaluation

We propose an expanded single-valued neutrosophic (SVN) EDAS model for multiple attribute decision-making (MADM) in this study. Firstly, we present an integrated method for deriving attribute weights relying on the entropy measurement and best-worst method (BWM) under the environment of single-valued neutrosophic sets (SVNSs). Then, combining the integrated weight model, we propose the computing steps of the SVN-EDAS evaluation framework. Furthermore, we explore the usefulness of the presented framework in innovation capability evaluation. Finally, a numerical example related to the innovation capability evaluation of energy enterprises is carried out.
A $145-\mu \mathrm{W}$ Always-on Touch Screen Readout and Always-on Haptic Driver IC for 1.26-Inch Circular Display of Wearable Device

This paper presents a touch screen panel (TSP) read-out and haptic driver IC designed for Always-on-Touch and Always-on-Haptic functionalities with ultra-low power consumption. To maintain always-on functions even when the application processor of the mobile device remains in sleep mode, a TSP analog front-end having a dedicated low power operational mode is proposed. Adapting to a circular display form factor, the mutual capacitances (Cms) sensed by the multi-driving method vary among the TSP receiving electrodes. Each base-capacitance compensator operates independently to compensate varying Cms, leading to an enhanced signal-to-noise ratio (SNR). The proposed system architecture has been realized using a 130-nm process, wherein it achieved an SNR of 51 dB with 400-Hz TSP scan rate under the normal TSP sensing mode. In the low-power TSP sensing mode, it achieved an SNR of 35 dB while consuming $145-\mu\mathrm{w}$ of power. The always-on touch-to-haptic response time was measured to be $790\mu \mathrm{s}$.
Improving the Performance of RRAMs: Insulator Layer Selections, Switching Characteristics, Conductive Mechanisms, and Perspectives

With the coming of the big data era, the demand for high-performance computing and memory devices has been explosively growing at every single moment. The Von Neumann architecture is facing its bottleneck and the size of transistors is close to their physical limitation because the energy consumed by accessing data is much higher than that consumed by operating data, which prevents improvements in computing speed and wastes energy. Resistive random-access memory (RRAM), with its compute-in-memory (CIM) structure and non-volatility, presents great performance with ultra-fast operation speed and low operating voltages and becomes the most competitive candidate for the next generation memory and computing. However, the selection of RRAM insulator materials and the control of the conductive mechanisms are too diverse to be well understood. In this paper, we will specifically focus on the switching performance across several crucial and promising oxide and nitride insulators as well as conductive mechanisms based on ions migration. We expect our paper to provide important guidance to the research of RRAM conductive mechanisms and insulator choices in the future.
Power System Steady State Model Validation Using Synchrophasors

In this research a model validation method for wind energy transmission systems using synchrophasors is proposed and implemented. A benchmark model based on the operational model of a regional transmission system operator was validated against actual PMU synchrophasor measurements. Synchrophasor measurements were provided by a utility company with significant PMU installations in the region. The model was provided by the regional transmission system operator. The model validation method ensures the accuracy of the model. The proposed model validation method is generic and can be used for any power transmission system for which synchrophasor data is available.
Classifying Bangla Health Misinformation from Social Media Using Machine Learning

The growing use of social media platforms offers a means to spread health-related information at an increased rate. However, disseminating health information on social media may be hazardous as the information often has no regulation. Therefore, it is crucial to find ways to classify health misinformation. Using machine learning models, this project utilizes mined Bangla text data and its English Translations to classify health misinformation from mined social media data. Multiple models were used such as Random Forest, XGBoost, SVC, etc, The highest reported precision in Bangla text and the English translation is 77 % and 79 % respectively. XGB classifier had the highest accuracy of 75% for English translation and the Extra Trees Classifier had the highest accuracy of 72 % for Bangla text.
Developing Simulation Capabilities for Supply Chain Cybersecurity of the Electricity Grid

The electricity grid has evolved into a cyber-physical system that integrates electric systems with advanced information and operation technology. With the frequency of cyber-incidents increasing in recent years, the need for secure and robust cybersecurity solutions for supply chain networks for critical infrastructures, such as the electricity grid, has intensified. This paper proposes a testbed that involves hardware-in-the-loop simulation of power system operations and realtime data acquisition and management using the OSIsoft PI System and a common set of power control devices. A discussion of the development of this simulator's capabilities for supply-chain cybersecurity is included. The simulator is used to emulate the operation of a remote terminal unit, an SEL 751 relay, a vendor, a utility, and an attacker to execute the power system adversarial operation in a power grid environment. The testbed can provide researchers with a streamlined method to assess the impact of adding security measures related to the supply chain to the electricity power grid.
High Frequency Audio Devices Based Inaudible Ultrasonic Hand Tracking System Using Subspace Method

This paper presents a signal preprocessing and target tracking system based on the Multi-Frequency Continuous Wave technique, enabling the perception of targets using Commercial Off-The-Shelf high-frequency microphones and speakers. The algorithm incorporates circular fitting and subspace methods to achieve static clutter suppression, target selection, distance imaging, and gesture tracking with the aid of particle filters. Experimental results demonstrate that the integration of the subspace method and particle filters results in a reduction of the measurement distance span error by 40% compared to the traditional phase measurement approach.
Log Analysis and Prediction for Anomaly Detection in Network Switches

In this study, we propose a three-step anomaly detection system for network switches. The proposed system consists of the following steps: 1) Log parsing, where log messages from switches are analyzed to identify patterns and events, 2) Analysis of the identified event flow to distinguish normal and abnormal event sequences, and 3) Prediction of the next log message, with detection of anomalies if the predicted log message differs from the normal log messages. For event classification, a log parser is proposed by modifying existing algorithms, and experimental results confirm that similar log patterns are correctly classified into the same event. To learn normal event sequences, both FSM and LSTM models are trained. Lastly, we proposed a BERT-LSTM model to predict the next log message and detect unexpected log messages. The proposed system is validated using data collected from a constructed testbed and achieves a high-performance level with an F1 score of 83.72%. Notably, our system achieved a recall of 94.74%. Our system has an advantage in that if misclassified cases occur, network administrators can retrain each model to improve precision during system operation.
Using Machine Learning for Target Detection and Matching in Dual Drone-Based Target Localization

The accuracy of target location measurement in multi-drone scenarios poses a challenge due to difficulty in detecting and matching image targets with high computational requirements. This paper proposes a machine learning-based approach for detecting and matching multiple targets in dual drone-based cross view scenarios, utilizing a combination of the SuperGlue model and the iterative reduction matrix element maximum threshold comparison (IRMEMTC) algorithm. The polar constraint concept is applied to pixel coordinates to measure the position of shared targets in dual drone images within real space using a target pixel positioning matrix. Additionally, the trisection method is used to determine the optimal target position, enhancing the method's resilience against potential sources of interference. Experimental results indicate that our proposed method offers a promising and systematic solution for reliable and accurate target position measurement using dual drones.