Miracle-dz/UniFAR-FTUs huggingface dataset:
Relay-Assisted Online Service Function Chain Placement and Resource Allocation in 6G Network

Service based architecture enables network function virtualization and flourishes 6G network with ubiquitous services and infrastructures. Multiple virtual network functions can be connected in sequence to create service function chains (SFCs), which implements automated configuration processes to reduce system delay and improve resource utilization. Due to the limitation of user transmit power, time-varying wireless channels, and the dynamics of resources, a relay is introduced to assist transmission and act as an orchestrator to collect global information of users and manage SFCs. However, the multi-level service architecture brings additional complexity to efficient resource allocation and SFC placement. This paper investigates a relay-assisted system with the goal of long-term delay minimization. Transmit power, bandwidth resource and SFC placement are jointly optimized under the constraint of network stability. Utilizing Lyapunov technique, the formulated long-term non-convex optimization problem is transferred into one-slot sub-problems including resource allocation and SFC placement, which are further solved by introducing auxiliary variables and Hungarian algorithm, respectively. Simulation results demonstrate that the proposed algorithm can significantly reduce system delay and achieve a $[\mathcal{O}(1/V),\mathcal{O}(V)]$ trade-off between system delay and queue stability.
Novel Algorithms for Filtering and Event Detection in Non-Intrusive Load Monitoring

The rise of the Internet of Things (IoT) and Industry 4.0 has brought about a connected ecosystem where instruments and devices are interlinked. Within this framework, an array of smart sensors and measurement units are present, resulting in a substantial amount of data. Consequently, data analysis has become a central focus across numerous research domains, including power systems. Non-Intrusive Load Monitoring (NILM) is a data-driven approach that aims to disaggregate total energy consumption into appliance-level information, thereby facilitating the balance between supply and demand in electrical grids. Notably, preprocessing assumes a vital role in data-driven methodologies. Firstly, preprocessing enhances the quality and accuracy of collected energy data, ensuring reliable inputs for subsequent analysis. Techniques such as noise reduction, outlier detection, and data cleaning eliminate unwanted anomalies, ultimately improving data integrity. Secondly, preprocessing enables feature extraction, facilitating the identification of distinctive patterns associated with individual appliances within the aggregated energy signal. This paper focuses on proposing novel preprocessing methods that encompass the aforementioned advantages, thereby contributing to the field of NILM.
Extended Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Battery

In recent years, there has been a growing concern over the escalating severity of global environmental degradation and the energy problem. The lithium-ion battery is extensively used in automotive applications owing to its extended lifespan and superior energy density. The measurement of the state of charge (SOC) of a Li-ion battery has significant importance as an indication. Ensuring the safe functioning of Li-ion batteries relies on the precise calculation of State of Charge (SOC). Nevertheless, the conventional technique of estimation, known as the ampere-hour integration approach, is susceptible to cumulative error and lacks the ability to sustain accurate outcomes over prolonged periods within an operational setting. Subsequently, the extended Kalman filter (EKF) approach is used to address the estimate inaccuracy. The test system has been constructed in MATALAB/Simulink in order to examine the effectiveness of the suggested technique. The simulation results demonstrate that the suggested technique outperforms previous methods in terms of performance and estimate SOC accurately across various working situations.
Culturally Sensitive Human-Robot Interaction: A Case Study with the Pepper Humanoid Robot

Socio-cultural factors play a significant role in achieving the trust and acceptance required to realize the potential of artifiical intelligence and robotics. Consequently, culturally sensitive human-robot interaction is essential for the adoption of social robots in Africa. Three elements must be present to achieve this: cultural knowledge representation, culturally sensitive planning and action execution, and culturally aware multimodal human-robot interaction. This paper presents a system architecture for culturally sensitive human-robot interaction in Africa that addresses these three elements, an initial set of software interaction primitives, and a practical demonstration of culturally sensitive human-robot interaction using the Pepper humanoid robot.
Seismicity in the Western Great Basin Apparently Triggered by the Landers, California, Earthquake, 28 June 1992

Within 24 hr after the Landers earthquake, there were three magnitude 3.4+ events in western Nevada and an unexpected, widespread increase in the rate of small events. Based on combined catalogs for northern Nevada, southern Nevada, and southern California, and a model that assumes statistical independence of events in these regions, the probability of this happening in a 24-hr period by random chance is less than ∼10−12 per day. Therefore, there is high statistical confidence that the increased seismicity was triggered by the Landers event. Based on the statistical model, we develop a list of 227 earthquakes in the first 83 days following the Landers earthquake, each of which has no more than 10% probability of occurring by random chance. The suspect events are broadly distributed in regions that correlate with historical activity in the Great Basin. The events are not uniquely associated with known volcanic activity, or with zones that were previously active with microearthquakes or aftershock sequences. The magnitudes of the largest triggered events appear to decrease with distance. With time, the number of suspect events decreases at a rate comparable to the rate of decrease of aftershocks of the Landers and Big Bear earthquakes. Three of the most significant triggered events that that occurred were as follows: Mina, 500 km from Landers, M 4, 36 min after Landers; Smith Valley, 590 km from Landers, M 3.4, 56 min after Landers; and Little Skull Mountain, 280 km from Landers, M 5.6, 22.3 hr after Landers. The evidence for triggering is particularly strong in the case of the Little Skull Mountain event, where an increased rate of microseismicity was evident as soon as small events could be identified in the coda of the Landers earthquake. We evaluate the seismic history for the past 3 yr to understand what was unique about the Landers earthquake. This includes identifying the previous earthquakes most likely to have caused the strongest shaking in various frequency bands. Based on a simple screening model, the strains from the Landers earthquake were uniquely large at low frequencies, but at high frequencies they were exceeded frequently by small, more local events. Thus, we hypothesize that the cause of triggering is low-frequency dynamic strains, at periods of about 10 sec or greater, and that there is a regional threshold that must be exceeded, since previous events with calculated strains that were almost as strong were not causes of widespread triggering. This mechanism for triggering satisfies the criterion of being a relatively rare phenomenon, since it is likely to occur only when a large long-period wave is radiated into an area where strain has been building slowly toward the point where faults are unstable.
Talking Without Talking

ABTRACT The Silent sound technology is a completely new technology which can prove to be a solution for those who have lost their voice but wish to speak over phone. It can be used as part of a communications system operating in silence-required or high-backgroundnoise environments. This article outlines the history associated with the technology followed by presenting two most preferred techniques viz. Electromyography and Ultrasound SSI. The concluding Section compares and contrasts these two techniques and put forth the future prospects of this technology. KeywordsArticulators , Electromyography, Ultrasound SSI, Vocoder, Linear Predictive Coding
Analysis of Distribution Areas to Achieve Positive Energy Communities

The Ecs (“energy communities”) provides a integration of distributed energy resources (DERs) and increasing acceptance. , infrastructure development. , and encouraging private investments and collaborations between the private and public domain. The connection of renewable sources near the places of consumption can lead to flexibility in changing the operating diagram. , the operator having the possibility to keep certain distribution cables disconnected, which diminishes the reactive power circulating through the distribution network. The article analyzed two types of distribution areas that show that in the area where consumption is high, it is easier to implement measures to transform the area into positive energy.
Spatio-Temporal Super-Resolution for CS-Based ToF 3D Imaging

Time-of-Flight cameras, especially the ones featuring sensors built upon macro-pixel architectures, are generally characterized by lower resolutions than other imaging modalities. In this work, we propose a practical single-frame super-resolution scheme to surpass this limitation by exploiting the architecture of a laboratory-designed macro-pixel ultra-high-speed image sensor. In addition, we propose a two-step sparsity-aware greedy algorithm for the recovery of the signal in the time (depth) domain. The main contribution of our algorithm is the introduction of a preliminary screening step to define a set of feasible support candidates in which we replace the demodulation functions, the assembly of which yields the sensing matrix, by the theoretical binary codes they are built upon. We empirically demonstrate that our algorithm improves the recovery performance with respect to Order Recursive Matching Pursuit for sparsity $\leq 2$.
Research on Usage Pattern of Task Lists for Open Source Communities

As open source collaboration platforms now offer a code branching-based contribution model, more and more open source projects are adopting this collaboration mechanism. The issue mechanism serves as a critical component in organizing tasks or defects within a project. However, when an issue's work-load is substantial and lacks effective task splitting and progress tracking, it can significantly hinder development efficiency and quality. While the task list mechanism introduced by GitHub holds promise as a potential solution, its application model requires further theoretical research. To address this research gap, this paper aims to explore the application patterns of task lists in the GitHub open source community. Specifically, this study selects three popular open source projects from the rust-lang community on GitHub and quantitatively analyzes their usage of task lists and their correlation with issue feature metrics. The study's results provide preliminary insights into the current state of task list application in the open source community and are intended to guide developers in their collaborative task management efforts.
1DCNN-TRSNet: A Hybrid End-to-End Arrhythmia Classification Deep Network Based on Transformer

Intelligent recognition of arrhythmias using ECG signals is crucial in the diagnosis and prevention of heart diseases. However, traditional methods for detecting arrhythmias rely on manual analysis by specialized physicians, which can be subjective and time-consuming. In this paper, we propose a novel end-to-end 1DCNN-Transformer hybrid deep learning model for the automatic recognition of arrhythmia types. Our proposed model employs a Transformer-based architecture to aggregate local contextual spatial features from a 1DCNN and incorporates temporal information in the high-level abstract features of the convolutional network output using positional encoding. The proposed deep learning model comprises a Transformer-Encoder and Transformer-Decoder that utilizes a multi-headed self-attentive mechanism to couple spatiotemporal features from different time segments and filter useful feature information. Each module converts input feature information into a higher-level abstract output, enabling the model to learn a complex abstract transformation function directly from the original ECG signal. Finally, the projected output is mapped onto the arrhythmia label space. Experimental results on the MIT-BIH Arrhythmia Database demonstrate that our proposed 1DCNN-Transformer network achieves excellent performance, with an overall average recognition accuracy of 99.46%, across five categories of arrhythmia signals: normal beat (N), right bundle branch block beat (R), left bundle branch block normal beat (L), premature ventricular contraction (V), and atrial premature beat (A).
Effect of Recursive Cluster Elimination with Different Clustering Algorithms Applied to Gene Expression Data

Feature selection (FS) is an effective tool in dealing with high dimensionality and reducing computational cost. Support Vector Machines-Recursive Cluster Elimination (SVM-RCE) is one of several algorithms that have been developed for FS in high dimensional data. SVM-RCE involves a clustering step which originally is k-means. Using various performance metrics, three alternative algorithms are evaluated in this context; k-medoids, Hierarchical Clustering (HC), and Gaussian Mixture Model (GMM). Comparisons will be carried out on five publicly available gene expression datasets. The results show that k-means in SVM-RCE obtains higher performance than other tested algorithms in terms of classification performance. Additionally, HC shows a similar performance to k-means. Our findings show superiority of using k-means. This study can contribute to the development of SVM-RCE with different variations, leading to decrease in the number of selected genes, and an increase in prediction performance.
Design and Performance Analysis of a 38 GHz Microstrip Patch Antenna with Slits Loading for 5G Millimeter-Wave Communications

In this paper, we present a compact, rectangular microstrip patch antenna with small rectangular slits designed for 5G wireless applications in the 38GHz band. The antenna is built on a Roger RT duroid 5880 substrate with a relative permittivity of 2.2, a height of 0.381 mm, and a loss tangent of 0.0009. The microstrip line technique is utilized for power feeding to the radiating patch. To enhance the antenna's performance, three slits are incorporated into the front side of the radiating patch. The design, simulations, and optimizations are conducted using the High-Frequency Structure Simulator (HFSS), while Computer Simulation Technology (CST) is used for validating the HFSS results. The antenna has a compact overall size of 6.12 × 6 × 0.381 mm3. Moreover, the proposed antenna exhibits impressive characteristics, including a resonance frequency of 38.67 GHz, a reflection coefficient (S11) of - 47.82 dB, and an impedance bandwidth of 2.35 GHz (37.45 GHz - 39.8 GHz). In addition, the antenna's gain varies from 7.84 dBi at the lower frequency of the operational band to 7.9 dBi at the higher frequency, with a maximum gain of 8.037 dBi at the resonance frequency. Likewise, the radiation efficiency is above 99% across the operational band. The results obtained from both simulators are in a good agreement and surpass those of many previously published antennas, indicating that the proposed antenna is a promising solution for 5G wireless communication applications.
A Comparative Study on Node Classification Methods For Undirected Social Networks

Traditional Neural Networks can solve problems with normal 1-dimensional and 2-dimensional Euclidean data such as image and text classification. However, most real-life problems are relationship-based and the corresponding real-world data has a non-Euclidean nature. This paper presents a comparative study between Graph Neural Networks (GNNs) and traditional approaches (K-Nearest Neighbour(KNN)) for solving real-life problems involving non-Euclidean data with complex relationships. The results demonstrate that GNN models outperform KNN in terms of accuracy while maintaining low runtime on graph structured data. The study highlights the strengths and limitations of Graph Convolutional Networks (GCNs) and GraphSAGE, with GraphSAGE offering flexibility and scalability but potentially introducing aggregation bias. Ongoing experiments include investigating the sensitivity of the algorithms to hyperparameter values. Overall, this research contributes to the understanding and practical application of GNN methodologies in various domains.
Triple Three-Phase Diode Rectifier-Connected Wound Rotor Non-Overlap-Winding Synchronous Generator for DC Grid Wind Turbine Systems

In the search for non-permanent magnet wind generator solutions, this paper presents a performance evaluation of a wound rotor synchronous generator (WRSG) with a nonoverlapping stator winding for DC grid wind turbine systems. The focus is to compare the WRSG performance when connected to single and triple three-phase rectifier configurations of DC grids, particularly in offshore wind farms, where DC grids are gaining prominence. The evaluation is conducted through simulations using Ansys Maxwell/Simplorer and experimental measurements. Both the simulations and experiments are performed on a WRSG prototype featuring a 16/18 pole/slot combination. The findings present a comprehensive analysis of various performance indicators,including torque, voltage and current waveforms, as well as efficiency. The results show that the triple rectifier configuration has a significant higher efficiency than the single rectifier configuration.
Design and Parametric Study of a GYSEL Power Divider for Mobile Applications

Power dividers are widely used in antenna arrays feeding, radar systems, communication systems, and medical imaging systems, which makes it indispensable microwave components. These power dividers have several design advantages such as low cost, lightweight and manufacturability. Among these power dividers, one can cite Gysel power dividers which are used for antenna array feeding with high power-handling capability. This work presents a parametric study of a standard design of a Gysel divider intended for mobile applications. To design this power divider, the ADS Momentum simulator has been used. During this parametric study, our goal is to show the effect of some design parameters such as physical dimensions and electrical properties on some GPD characteristics in terms of bandwidth, power ratio, and isolation. The proposed structure consists of standard Gysel power divider of three ports and operating at the frequency of 2.4 GHz.
AI-Based Classification of Normal and Aggressive Behaviors using EMG Signals

Behavior analysis using Electromyogram (EMG) signals is an essential step in understanding aggressive behaviors, the physiological behavior of the neuromuscular system, and its applications in other disciplines. Therefore, this study aimed to develop a model for detecting behaviors using EMG signal analysis. This model utilized a dataset consisting of EMG signals from eight channels obtained during a series of activities on four subjects. Accordingly, a classification model was developed to detect specific features of behaviors and differentiate between normal and aggressive behaviors by analyzing the EMG signals. The model was developed by incorporating signal and statistical features and applying different machine-learning techniques. A total of 44 models were evaluated for their performance. The Support Vector Machine (SVM) classification model developed using all extracted features achieved an accuracy rate of approximately % 91 in behavior classification. The obtained results demonstrate the potential of EMG signals as a tool for behavior detection and classification.
Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder

We present in this paper a novel method for fault detection and classification in power transmission lines based on convolutional sparse autoencoder (CSAE). In contrary to conventional methods, the proposed method automatically learns features from a dataset of voltage and current signals, on the basis of which a framework for fault detection and classification is created. Convolutional feature mapping and mean pooling are implemented in order to generate feature vectors with local translation-invariance for half-cycle multi-channel signal segments. Detection and classification are done by a softmax classifier using the feature vectors. Further, the proposed method is tested under different sampling frequencies and signal types. Results show that the proposed method is fast and accurate in detecting and classifying faults, and is practical for online transmission line protection.
Beyond Clean Phase: Using Silence-Generating Phase for DNN-Based Speech Enhancement

Speech enhancement algorithms usually operate in the short-time Fourier transform (STFT) domain and only enhance the magnitude spectrum, while adopting the noisy phase for synthesis. This is because the phase has often been considered unimportant. However, recent findings have proven otherwise, leading to an improved enhancement by considering the phase either implicitly or explicitly. In this paper, we propose a phase-aware extension of our recently published two-stage speech enhancement approach. It comprises, among other improvements, an additional explicit phase estimation stage whose structure is inspired by the fundamental ideas of our work on phase reconstruction. Unlike most phase-aware approaches, we do not estimate the clean phase but propose a novel combined consistent-inconsistent phase (CIP). It corresponds to a silence-generating phase for the noise-dominated time-frequency (TF) parts and thus allows noise reduction without modifying the magnitude spectrum at all. We show that this new CIP can provide a significant performance improvement compared to the clean phase. Experimental results confirm the effectiveness of our proposed extensions, ultimately leading to improved speech quality (PESQ, DNSMOS) and speech distortion (segmental SNR).
Near-field Characterization of a GHz Branchline Coupler Using a THz Microscope

The design of microwave circuits is usually based on simulations, which can be supported by S-parameter measurements to evaluate the circuit’s performance after fabrication. Some characteristics of complex designs cannot be investigated this way, but advanced measurement methods can improve the design process significantly. In this work, a THz microscope is used to characterize a microwave network consisting of a branchline coupler and multiple patch antennas in a frequency range of 20 to 40 GHz. The radiation intensity above the patch antennas is spectroscopically measured and compared to conventional S-parameter analyses. The near-field pattern of the circuit is spatially measured and compared to corresponding simulations. The THz microscope is a versatile tool that can be used to validate simulation results and improve the overall microwave circuit design process.