Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV-Satellite Integration for Communication System: Potential Applications and Key Challenges

Unmanned aerial vehicle (UAV) and satellite integration has become an evolution in communication systems. This study explores the synergistic potential of UAV-satellite integration, along with its applications, difficulties, and potential future prospects. While satellites provide a wider geographical reach, UAVs increase localized coverage and flexibility. This combination shows potential for numerous applications like disaster response, environmental monitoring, remote connectivity, and scientific research campaigns. Coordination of the network, energy efficiency, security, signal interference, and many more are currently the issues we need to pay attention to in this system. Swarm intelligence, AI-driven decision-making, and hybrid communication architectures are promising study areas in the future. As the UAV-satellite integration process advances, it reshapes communication standards and offers unprecedented efficiency and connectivity.
A Compact Weak PUF Circuit Based on Random Process Deviations of Amplifier Chain

Physical Unclonable Function (PUF) circuit is one of the effective security primitives for low-cost solutions such as key storage, chip authentication, and supply chain protection. Through the research on PUF circuits and amplifiers, this paper proposes compact PUF circuit design scheme based on random process deviations of amplifier chain. First, the sensitivity of the maximum gain point of the common-source amplifier using diode-connected PMOS as a load reveals the input-output mismatch mechanism caused by the deviation of the maximum gain point of the amplifier. Next, combined with the transmission gate to change the amplification path and the controllable gate voltage, a nonlinear output function that changes with the challenge signal is formed, and a compact PUF circuit based on the amplifier chain is designed to improve the randomness, uniqueness and stability of PUF output response. Finally, the proposed PUF is designed with TSMC 65nm CMOS process and verified by Spectre. It has good stability and the cell layout area is 0.849 μm2 which is 52% lower than similar circuits.
Pavlovian Conditioning Modeling Using Wireless Spiking Neural Network

To address the communication congestion and high energy consumption issues in IoT networks, prior research has proposed a distributed information processing system called WiBIC. As an exploratory step towards implementing the basic functionalities of the WiBIC system, this study focuses on the wireless integration of SNNs. Considering the data transmission and learning characteristics of SNNs, we propose using the APCMA protocol to effectively stabilize the operation of the wireless SNN. By modeling Pavlovian conditioning on this network, the learning capabilities of the implemented system can be determined.
Optimal Planning of Battery Swapping and Charging Stations for the Urban Cities

Electric mobility is becoming necessary for urban zones with ever-increasing fuel costs and rising air pollution levels. Location planning of Battery Swapping and Charging Station (BSCS) for battery electrical buses (BEBs) requires to consider many factors. The existing research focuses on social cost model, vehicle relocation problem and local charging stations. However, battery swapping stations with charging stations, and urban routes have not yet been studied for BSCS to charge BEBs. In order to solve above problem this paper presents a selection of sites for optimal placement of battery swapping and charging stations in urban cities considering the routes and location of bus depots for battery electric buses. The optimal location of BSCS for BEBs has the effect of reducing the overall investment cost and charging cost. Secondly, this paper establishes a BSCS location optimization model which simplifies into a road network to meet the maximum accessibility of BSCS to the BEBs. Finally, to identify the optimal location for BSCS, minimum total construction cost of BSCS considering land price and charging station is discussed in this article. The real-world road networks of Pune city are taken for parameter analysis.
DOA-Estimations-Based 3D Geolocations Using Factor Graphs

This paper proposes direction of arrival (DOA)-estimation-based three-dimensional (3D) positioning techniques using factor graphs. The multiple signal classification (MUSIC), root-MUSIC, and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms are used to estimate the DOA of the signals received by multiple sensors. A factor graph (FG) detector updates and refines the measured DOA for obtaining the target positions. Since only the mean and the variance of the DOA are needed for the factor graph process, the burden of the data transmission of the uplink from the sensors to the fusion center and the complexity of the positioning is reduced significantly. The effectiveness of the proposed method is demonstrated through simulations. No matter which DOA estimation method is used, MUSIC, root-MUSIC, or ESPRIT, FG algorithms achieve higher positional accuracy compared to the linear least square (LS) positioning solution in terms of the root mean square error (RMSE).
DDPG-based Multi-UAV Trajectory Optimization for WSN’s Data Collection

Unmanned aerial vehicles (UAVs) have the advantages of low cost, flexible deployment and stable transmission links, which are widely used in wireless sensor network (WSN) for data collection tasks. This paper builds a UAV-assisted data collection model in WSN, and investigates the trajectory decision-making problem of the UAV swarm, which is a multi-objective optimization problem for simultaneously maximizing the collected data and minimizing the energy consumption of the flight. In particular, we introduce deep deterministic policy gradient (DDPG) algorithm to solve the UAV flight decision-making problem. Through multi-agent synchronous training, it can adapt to multi-agent reinforcement learning environment, so as to obtain the promising strategy of multi-UAV flight trajectory. According to experimental simulation, we compare two contrast algorithms with the DDPG-based UAV trajectory algorithm, and verifies the enhanced performance gain.
Multivariate Time Series Forecasting Based on Fuzzy Information Granulation and LSTM

Effectively mining features from multivariate time series data to accurately realize multivariate time series forecasting has become a challenging topic. In order to better extract the representative information in multivariate time series, this paper proposes a new prediction model based on fuzzy information granulation and long short-term memory network, which realizes the fine-grained prediction of multivariate time series from series to points. The model firstly partitions multivariate time series into a set of time windows. Secondly, in order to capture the expected information hidden in the time series, we use triangular fuzzy granulates to granulate the fuzzy information of each sub-time series of multivariate time series. Furthermore, the maximum value (upper bound of the interval), the average value and the minimum value (lower bound of the interval) of each sub-time series can be extracted after granulation to construct the information grain for multivariate time series prediction. Finally, it is combined with the long short-term memory network to predict the target variable in multivariate time series. The proposed model is validated using energy time series, and the results show that the method is more accurate in predicting the real-time energy demand of the grid.
On Board Data Analysis and Realtime Information System

For many information applications like alarming services or surveillance systems, reactivity is a very important criterion. This is also true for many applications based on satellite data, for example in the field of remote sensing or space system operation. These applications are currently bottlenecked by classical satellite operation, where contact to the satellite is only established within a few communication windows, when the satellite is in reach of an assigned ground station. The amount of time between collection of the data on the space system and having it available for ground-users can take from several hours up to days.At our institution, we are working on a cost-efficient method to decrease the latency until information, derived from satellite data, becomes available for usage on ground. The basic approach is trying to utilizing satellite based low-latency real-time telecommunication networks, designed for ground-based satellite phone usage in combination with on board information extraction. Information extraction on the satellite will be necessary as the telecommunication networks only offer a very limited data-transfer bandwidth in the range of a few hundred bytes and the transmission of raw sensor data is not possible.
Recursive Gate-Controlled Convolution for Remote Sensing Detection

Remote sensing (RS) multi-class object detection holds great potential in various fields such as agriculture production, urban planning and military reconnaissance. Recent research on RS object detection has primarily focused on utilizing multi-scale feature while overlooking the unique prior knowledge in global information. This prior knowledge can be valuable since small RS objects may be incorrectly detected without sufficient reference to global information, and different types of objects may require different global information. In this paper, we address this prior factor by introducing recursive gate-controlled convolution (RGC). RGC possesses the capability to model global information, allowing for the recursive detection of spatially adjacent features related to the objects through simple operations. By doing so, it better emulates the detection performance of various objects in RS scenario. Additionally, recognizing the crucial role played by multi-scale features in dense scenes, we have preserved this knowledge and enhanced the interaction between features using the bidirectional feature pyramid. Our experimental results on the FAIR1M have achieved competitive performance (68.0% mAP), significantly surpassing the methods employed in FAIR1M.
Lower body action classification using unlabeled predicted motion

In this study, a rule-based motion classification method was presented for the lower extremity motions prediction and classification for exoskeleton robot lower extremity support. With the CMU public DB and additional motion DB using Xsens device, the joint angles of the lower extremity for the next second was predicted using the bidirectional LSTM algorithm. By defining the motion vector of the predicted lower extremity joint angle and calculating the motion score, future lower extremity motions could be classified as walking, squat, and stoop without labeling process. The proposed algorithm is fast in calculation and does not require label work, so it can be easily loaded into low-cost devices, so it is expected that it can be applied not only to exoskeleton robot control but also to wearable smart devices
Supporting ICN in 5G Core Network

ICN effectively delivers content, such as multimedia and IoT, by name-based routing. Recently, works on supporting ICN in 5G network have been actively studied for effective data delivery in 5G. However, they mainly focus on interworking network architecture and extended NF and interface to support ICN, and little works have been carried out for a detailed procedure supporting ICN in 5G network, to the best of our knowledge. In this paper, we propose content name registration, ICN PDU session establishment, and Interest/Data packet delivery procedures to support ICN in an extended 5G core network.
Adaptive Routing of Task in Computing Force Network by Integrating Graph Convolutional Network and Deep Q-Network

Computing Force Network (CFN) is an emerging technology that has become a hot research topic, with the explosive growth of ultra-low latency and real-time applications with specific computing and network requirements. The main challenge of CFN is to jointly use network and computing resources, and it is difficult to achieve real-time routing to meet the different requirements for different services. In this paper, an Adaptive Routing of task by integrating Graph Convolutional Network and Deep Q-Network (AR-DQN-GCN) is proposed for Computing Force Network, which combines the network and computing resources to provide fine-grained, real-time and dynamic adaptive routing decision. We integrate GCN into Deep Q-Network (DQN) model to obtain topological features from graph structure states. Meanwhile we design an adaptive mechanism, which can adaptively adjust the routing reward mode according to the types of services. We evaluate our model through experiments with representative network topologies.
Leaf Diagnosis: A Lightweight Image Recognition Model for Detecting Crop Diseases

Identification of crop diseases is crucial for smart agriculture, as it directly affects crop yield and quality. With the rapid development of deep learning technology in image processing, using deep learning to identify crop diseases from images has become increasingly popular. However, traditional CNN models have large training parameters that cannot be applied to hardware-limited scenarios. To address this problem, we propose a lightweight YOLO crop disease recognition model–Leaf Diagnosis. The proposed model simplifies the model structure and expands the model’s application scenarios while ensuring model recognition accuracy. Specifically, it uses the lightweight Ghost bottleneck module as the backbone network and neck network of YOLOv5s to slim down the model structure. Additionally, the channel attention mechanism is used to improve model accuracy and compensate for the accuracy loss caused by the reduced model complexity.
Multimodal Mental Workload Classification Using Maus Dataset

This study aims to use machine learning to detect stress in individuals to enhance their quality of life, through stress management techniques. We propose supervised learning-based classifiers model for the detection of stress in humans using a MAUS dataset which is multimodal and is publicly accessible. MAUS focuses on the investigation of wearable device-based mental workload (MW) assessment system. The database contains records of fingertip PPG, galvanic skin reaction (GSR), electrocardiography (ECG) and wrist-worn PPG. For multimodal fusion, the normalized features of ECG and PPG (fingertip and wrist) are concatenated, and early fusion based classifier methods are applied. The accuracy and F1 score for combined modalities namely, ECG, PPG (fingertip) and PPG (wrist) is reported at 75.7% and 73.9% using AdaBoost Classifier.
Optimal Control of Wave Energy Conversion Supplemented by the Adaptive Compensation

Under conditions of irregular waves, the enhancement of the optimal power capture state can be achieved through the construction of the optimal power capture configuration using the extremum of the energy function. By employing fluid simulation software, a nonlinear hydrodynamic model is developed for the corresponding wave power generation apparatus. This model serves as the foundation for establishing an optimal electromagnetic force, guided by a magnetic field directional control strategy. To surmount the intricacies of the maritime environment, a super-twisting sliding mode observer is introduced, affording sensorless technology and elevating the precision of state variable acquisition. Simulation results unequivocally demonstrate that the envisaged control strategy exhibits prompt response dynamics and formidable adaptability when exposed to uniform wave excitation. Consequently, this strategy can significantly amplify system output power by approximately 40%, concurrently addressing a 10% compensation of power losses.
Multi-Path Routing Algorithm with Joint Optimization of Load-Balancing for Cluster-Based Leo Satellite Networks

In recent years, low Earth orbit (LEO) satellite networks have emerged as the primary service provider for future mobile communication systems. As the network scale increases, existing routing algorithms have become increasingly time-consuming, resulting in network load imbalance and potential communication delays that may not meet the needs of users. Therefore, this paper proposes a multi-path routing algorithm to jointly optimize the Quality-of-Service (QoS) and the load-balancing for cluster-based satellite networks. To account for the rapid changes in the satellite dynamic topology, an improved clustering structure is designed with two distinct phases: initialization and maintenance. Our strategy utilizes the concept of path deviation in the recursive method to quickly identify a multi-path routing that meets the QoS constraints and evenly distributes the traffic, thereby ensuring network stability and minimizing computational overhead. Simulation results indicate that the proposed algorithm significantly improves load-balancing in satellite communication traffic and enhances QoS for users. Overall, the algorithm represents a promising solution to the increasingly complex routing challenges posed by LEO satellite networks and demonstrates the potential to enhance the quality and efficiency of mobile communication systems in the future.
The loss of intervocalic short /h/ in Estonian

Estonian is a Finno-Ugric language that is characterised by the voicing or loss of both word-initial and intervocalic short /h/. The present paper aims to determine the acoustic characteristics of Estonian words where a short intervocalic /h/ has been lost. Disyllabic CVhV-words where the loss of /h/ has occurred are analysed. Two tendencies have been found: the loss of /h/ results either in a long monophthong or diphthong (CVV) or in a boundary between two vowels (CV.V). In most cases, the duration of V2 is longer than V1 but there are also variants where the loss of /h/ has lengthened the duration of the first vowel. Variants with a boundary are characterised by a slight dip in intensity between two vowels. Both variants’ pitch contours resemble those of Estonian quantity 3 words that are characterised by an early F0 turning point and falling pitch in accented words.
A High Input Impedance Low Noise Amplifier Capable Of Handling 1V Electrode Offset for Biopotential Recording

This paper presents a low noise amplifier (LNA) for neural signal with a newly proposed feedback network to improve the input impedance of the amplifier (0.5 GΩ). By combining with DC Servo Loop (DSL), the proposed LNA can suppress a maximum electrode offset of up to 1 V. The simulation shows that the LNA archives an input-referred noise of 1. 17u over the frequency bandwidth of ECG Signal (0.5 Hz to 100 Hz).
Distributed Projection Algorithm for Solving Variational Inequalities

In this paper, we study a distributed computation model to solve a system of variational inequalities corresponding to multiple agents. Each agent knows a part of the full mapping and is allowed to communicate with its neighbors through a time-varying communication network. To solve this problem, we propose a consensus-based distributed projection gradient algorithm relying on local computation and communication at each agent. We show that this algorithm is able to generate an approximate solution to these variational inequalities under mild assumptions.
Sensorless Indirect Voltage Control of a Buck Converter Feeding an Unknown DC Load: Predictive Digital Current Control

This paper presents a predictive digital current control strategy for regulating the output voltage of a DCDC buck converter feeding an unknown DC load current. The proposed control includes a PI controller to compute the inductor current reference based on the error between the desired and measured voltage. Additionally, two observers are implemented to estimate the buck converter’s DC load current and input voltage to ensure convergence to real values. Numerical simulations using MATLAB/Simulink show that the proposed control effectively regulates the output voltage even with variations in input voltage and DC load current. Furthermore, the proposed control is compared to the cascade loop PI controller and performs better than the PI control.
Complementary Model Fusion for Enhanced Social Popularity Prediction

Predicting the popularity of social media posts is an important and challenging task. However, existing approaches have limitations in effectively modeling the multimodal nature of posts and fusing diverse predictive signals. This paper proposes a novel deep neural network framework that integrates comprehensive features across textual, visual, social, and temporal modalities to predict photo popularity on Twitter and Flickr. The model employs parallel convolutional neural network streams to separately learn representations from visual and textual features. A late fusion scheme is introduced to combine these representations in a joint embedding space. Additionally, self-attention mechanisms are incorporated to focus on the most informative signals for popularity prediction. Through extensive experiments on large-scale Twitter and Flickr datasets, the proposed approach is shown to significantly outperform existing methods relying on individual modalities. Ablation studies validate the contributions of the key model components including multimodal feature extraction, late fusion, and self-attention. The results highlight the importance of integrated modeling with joint multimodal learning for accurately predicting social media virality. The techniques presented provide an effective and general framework for popularity prediction tasks involving diverse heterogeneous data. This work represents a significant advance in harnessing multimedia analytics and neural multimodal fusion to achieve state-of-the-art performance in predicting social media popularity.
A Regional Analysis of Climate Change Impacts on Canadian

Climate change is expected to alter production opportunities facing agricultural producers. Global studies of climate change impacts on agriculture suggest positive benefits for Canada. Results from Canadian studies tend to be more pessimistic; however, most of these studies are regionally specific and focus on the impacts on specific crops, particularly grains and oilseeds. This paper examines the impact of climate change on Canadian agricultural land values. Changes in land values are used to impute expected changes to agricultural GDP. We find that all provinces benefit from climate change and that previous estimates may be overly pessimistic.
Generative AI for Radiological Image Data: Current Trends and Outlook

Generative artificial intelligence (AI) has gained prominence, particularly in the field of radiological image data. This study investigates current trends and prospects of generative AI in X-ray, CT, MRI, and PET imaging. Emphasizing the importance of data quantity and quality, we ensure reliable and useful outputs. Given the significance of ethical dimensions in generative AI for radiological imaging, ethical considerations are also addressed. By analyzing recent advancements, this study offers insights into the evolution of generative AI in radiological image data and future directions.