Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fundamental Discussion on Reward-Based Reinforcement Learning for Uplink Resource Allocation in NTN

The integrated usage of non-terrestrial network (NTN) nodes like high altitude platform (HAP), and low earth orbit (LEO) satellite plays a significant supplemental role in the upcoming sixth generation (6G) network to provide connectivity services for massive mobile users. Various network functions can be performed intelligently using potential artificial intelligence (AI) tools. Resilient and effective network channel accessibility and resource management, however, pose significant challenges due to the mobility, scarce resources, and complexity of integrated terrestrial networks (TNs) and NTNs, and massive mobile users on ground networks. Considering that many services demand real-time uplink (UL) data transmission, in this work, we consider the uplink of cooperated air-space segments (HAPs and LEO satellites) for large-sized data transmission where HAPs transmit data simultaneously to the LEO satellites through multiple sub-carriers using non-orthogonal multiple access (NOMA) approach, and the LEO satellite receives the superimposed signal data from HAPs. To maximize the fair allocation of resources among HAPs while reflecting the LEO satellite's load and utility, the problem is formulated as nonlinear mixed-integer programming (MINLP). Given that NTN channels are dynamic, we advocate a novel deep reward-based multiple-agent reinforcement learning for dynamic channel allocation. Furthermore, to handle the short visibility of the LEO satellites in learning, we employ a prioritized experience reply (PER) through a priority sampling approach (PSA). Numerical results reveal that our proposed approach significantly outperforms learning-based and conventional baseline models.
Coupled Matrix Tensor Factorization via a Semi-Algebraic Solution Based on Simultaneous Matrix Diagonalization (SECSI -CMTF)

Recent research has shown that the joint analysis of heterogeneous data can be beneficial to understand the underlying structure of the data compared to a separate analysis. This research direction has gained high interest due to the tech-nological progress, where massive amounts of data from multiple sources are collected, e.g., multimodal data from a patient such as EEG (electroencephalogram), MAG (magnetoencephalogram), and other data gathered from laboratory tests. This task of data fusion is challenging due to the heterogeneous structure of the data. In this study, we perform a joint CP decomposition of a heterogeneous data set, i.e., a matrix coupled with a three-dimensional tensor along the first mode, via a new formulation of the coupled matrix and tensor factorization (CMTF) based on the SEmi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization (SECSI). In comparison with the traditional alternating and gradient-based optimization algorithms, the proposed SECSI-CMTF algorithm shows an accurate and robust performance with a significantly increased computational speed. The results are evaluated on synthetic data set and compared to other state-of-the-art approaches, also in ill-conditioned scenarios and in scenarios with different SNRs.
Restoring Engagement in Human-Robot Interaction: A Brain-Computer Interface for Adaptive Learning with Robots

This paper investigates the efficacy of a passive Brain-Computer Interface (BCI) in enabling a robot tutor to adaptively respond to a user's engagement level in real-time. The BCI system extracted EEG Engagement Index from the user's electroencephalography (EEG) signals as an indicator of engagement during Human-Robot Interaction (HRI). A within-subjects study was conducted in which the robot performed attention-recapturing behavior during a learning task under two conditions; either in an adaptive manner whenever a lapse in the user's engagement level was detected by the BCI system (Adaptive condition) or at random intervals regardless of the user's mental states (Random condition). In both conditions, users completed an information retention test following the interaction. The study found no significant difference in the postinteraction test results or mean EEG Engagement Index values between the Adaptive and Random conditions. However, analysis of 10-sec time windows following robot interventions showed that adaptively timed gestures were significantly more effective in restoring user engagement to optimal level compared to randomly timed gestures. This finding provides evidence for the potential of passive BCIs in improving user experience in pedagogical HRI settings.
Multi-node joint communication and radar using synchronization signals in 5G

Joint communication and radar (JCR) provide the mutual benefits of sensing and communication with shared hardware and spectral resources. We propose a JCR system with multiple nodes that leverages the 5G communication waveform for range estimation. We analyze a multi-node scenario where each node obtains an individual range estimate and then shares the estimate with other nodes to estimate each node's distance from the target jointly. We propose a linear minimum mean squared error (LMMSE) based estimate that combines the information from all nodes. The estimate can also be adapted to account for quantized feedback. The analysis and simulations for the model show that the proposed method outperforms the baseline models for both quantized and non-quantized methods. The distributed JCR results show that the transceivers in the system can detect the target more robustly, reducing the error in detection by 75%, as compared to the conventional approach.
Human-Object-Robot Interaction Through Playing Behaviour for Social Robots

Inspired by a common behaviour pattern from the dog-human relationship, we present a behaviour model combined with a method for controlling a real mobile robot, effectively realizing physical Human-Object-Robot Interaction. Translating social behaviours from the dog-human relationship into social robots has been already found to be a viable way. Playing, a cardinal behaviour element in the dog-human relationship, turned out to be one of the highest priority factors in forming a successful and lasting relationship between dogs and humans. Accordingly, we modeled the playing behavior when a human repeatedly initiates playing with a dog by picking up and throwing an object (toy), expecting the dog to go for the object and bring it back to the person throwing it, commonly known as playing fetch. We also developed a robot control system able to execute the commands from the behaviour model, and for gathering the poses of the participants in the real physical space we have used the What-You-See-Is-What-You-Get Indoor Localization (WIL) system. The integration of these components allowed us to create a social robotics system able to offer real physical human-object-robot interaction fast enough to be enjoyable for human participants, as a tool to be used in future HRI experiments, which was our main goal. The proposed behaviour model is intended to be embedded into social robots with the purpose of applying it in e.g. therapy, child-rearing, child-robot interaction scenarios, human behaviour assessment, recreational or entertainment scenarios.
Komering - Indonesian Machine Translation Using Embedding RNN

The Unitary State of the Republic of Indonesian has diverse language cultures across its regions. One of these is the Komering language culture in South Sumatra. Although the Komering tribe continues to use the Komering language, its speakers have decreased over time. Hence, developing a machine translation model for the Komering language is necessary. This research aims to develop a neural machine translator using the embedding RNN algorithm. The study evaluates several embedding RNN models to identify the model with the best accuracy performance. The best model produces results with an accuracy reaching 0.9583 during the training stage and a validation accuracy of 0.8866. These values indicate that the model can effectively translate sentences from Indonesian to Komering and vice versa.
Predictive Storage Management for Cloud-Based Video Streaming Using ML ARIMA Model

The exponential increase in video streaming has overburdened data centers, making efficient video streaming in the cloud essential. One potential solution is the tradeoff between transcoding and storing a video stream, as storing operations are cheaper than transcoding. This research explores the use of machine learning ARIMA algorithms to predict the number of views for each video in the repository for the next period of time. This enables informed decisions about which videos to keep or delete from the cloud, ultimately reducing cloud service costs. The study evaluates the accuracy of the ARIMA model in predicting future views for three datasets of varying sizes and levels of variability. Our results show 82% of predicting accurate views, indicating that the ARIMA model can effectively capture the overall trend of video views, providing valuable insights for service providers looking to optimize their storage management practices and reduce costs. This research contributes to the development of more cost-efficient video streaming in the cloud, which is essential for meeting the increasing volume of video stream traffic. By leveraging the ARIMA model, cloud-based platforms can make informed decisions on storage management, enhancing their overall service quality and user experience.
Wireless Stimulation of Motor Cortex Using an Ultra-Thin Implant Fabricated on Parylene/PDMS

In this paper, we describe the design and in-vivo testing of a wireless, battery-free implant that is fabricated on a $100 \mu\mathrm{m}$-thick parylene/PDMS substrate with dimensions of $15.6 \times{6}. 6$ mm2. The benchtop test shows that the device can generate monophasic voltage pulses with an amplitude of 20 V at a distance of 25 mm from an RF transmitter, and the frequency and width of these pulses can be externally controlled. The implant's operation was verified in-vivo by generating high-amplitude voltage pulses (8 V) and injecting adequate current (≈ 2 mA) into the motor cortex, leading to a motor response in a rat model. These results demonstrate the implant's ability to eliminate the need for batteries and wires and deliver electrical stimulation.
Research on calculation method of electric distance for AC and DC power system

Electrical distance is the key electrical index in AC/DC power system, which is of great significance to the planning, construction and operation of power system. However, the results obtained by different calculation methods are often not equal or even different. This paper describes the principle of the existing representative calculation methods for electrical distance calculation, and analyzes the reasons for the different calculation results of these methods. Aiming at the defects of some algorithms in principle, an improved algorithm is proposed. Finally, the following results are obtained: (1) The concept of classical electrical distance is extended to AC/DC power system, and a sensitivity based electrical distance calculation method is proposed. (2) Aiming at the shortcomings of the existing transient method, an improved transient method is proposed. (3) The sensitivity method proposed in this paper can reflect the influence of the topological structure and the DC control mode on the electrical distance under small disturbance; The transient method proposed in this paper can reflect the influence of topological structure, DC control mode and stability parameters on electrical distance under large disturbance.
A Quick Review of Human Perception in Immersive Media

With the development of multimedia and information technology, immersive media has been increasingly popular and attracted great attention. The concept of immersive media includes Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), 3D content, etc., which aims to provide or introduce immersive experience to the real-word through influencing human perception. Accordingly, recent years have witnessed an explosion of the literature on human perception in immersive media, including visual perception, auditory perception, etc. In this paper, we review the state-of-the-art works on studying human perception in immersive media and discuss corresponding applications. First, this paper reviews the perception attention studies of immersive media from the aspects of visual attention and multi-modal attention. Second, we survey the subjective and objective perceptual quality studies of immersive media including both visual quality assessment and multi-modal quality assesment. Finally, we overview the human perception properties in immersive media and outlook the future trends.
A Sequence Model Compression Method Based on Proper Orthogonal Decomposition

We propose a method that can compress the parameters and floating point operations (FLOPs) of a Long Short-Term Memory (LSTM) model to a third of the original model and get an acceptable error without fine-tuning. The LSTM can be treated as a discrete nonlinear dynamical system and use a kind of model order reduction method which is named proper orthogonal decomposition (POD) to deal with it. Approximating the hidden state reduces the dimensionality of the hidden state. We design a small LSTM model that can be considered as a time series classification model and a public dataset to validate the method. The number of parameters of the original model can be compressed to 34.4% and the FLOPs can be reduced to 25.6% with a 5% loss in accuracy.
Classification of Hypertension Levels Based on Photoplethysmography Signals Using Convolutional Neural Network (CNN)

Hypertension is a serious medical condition that has the potential to elevate the risk of diseases affecting various organs. According to statistical data from the World Health Organization (WHO), in 2019, cardiovascular diseases such as heart, brain, kidney, and other organ diseases have significantly increased due to hypertension, affecting approximately 1.4 billion people worldwide. Blood pressure monitoring is needed to reduce the risk, considering other parameters such as gender, age, height, weight, heart rate, and Body Mass Index (BMI). In blood pressure monitoring, many recent studies have used Photoplethysmography (PPG) signal for blood pressure measurement due to its easy-to-obtain and more accurate measurement. In this research, we will employ a Convolutional Neural Network (CNN) to analyze PPG signal characteristics and make predictions for both Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP). Additionally, a CNN architecture will be harnessed to categorize hypertension levels based on the forecasted blood pressure, as well as various factors such as gender, age, height, weight, heart rate, and BMI. This investigation will categorize hypertension into Pre-Hypertension, Normal, Hypertension Stage 1, and Hypertension Stage 2, utilizing a dataset consisting of 219 patients data and 657 PPG signal recordings. Using the proposed method, hypertension classification using CNN with classification report: accuracy 95%, F1-score 94,6%, recall 94,6%, precision 95.1%. The result shows that the proposed method is a promising solution to classify hypertension based on the Photoplethysmography signal effectively. This method can assist medical professionals in making clinical diagnoses with greater accuracy.
Intelligent Force Control for Robotic Tasks in Varied Unknown Environments

This paper introduces an intelligent impedance controller tailored specifically for redundant robots engaged in physical interactions. The controller enables the robots to perform predefined tasks accurately by regulating their parameters in unknown environments. The intelligent impedance controller is based on the fuzzy Mamdani method and is designed to accurately track the desired force in different environments. By utilizing appropriate fuzzy rules, the controller dynamically adjusts both the robot’s behavior and controller parameters. Extensive simulations are conducted using a 7DOF KUKA LWR robot arm to demonstrate the effectiveness and superiority of our intelligent controller over the classical method. The results show a notable reduction in steady-state force tracking error by 60 to 98% compared to the classical method, as well as improved tracking of the desired force. Meanwhile, settling time is improved considerably.
A +0.35 °C / -0.22 °C Inaccuracy CMOS-Based Temperature Sensor for IoT Applications

This work proposes a fully-integrated and low-power temperature sensor in a 180 nm standard CMOS technology. The dual loop clamping circuit and diode stacking structure are proposed to improve the accuracy. A dual input capacitor multiplexing Time-to-Digital Converter (TDC) is proposed to improve the resolution achieves. The simulated results shows that the inaccuracy is as low as +0.35 ° C / -0.22 ° Cover 0 ° C~ 120 °C with 0.08 °C-resolution, consuming only 246 nW at a 1.2 V supply voltage.
Improving water quality by removal of pesticide pollutants with aquatic plants

Several species of algae (axenic) and aquatic vascular plants were evaluated for effectiveness in removal of pesticides from water. Pesticides with 14c were utilized to determine plant uptake and molecule degradation. Herbicide concentrations below toxic levels were used; however, these were many times greater than natural residue levels encountered in surface water. It was evident that the plants removed herbicide residues from water, supporting the fact that residues have not yet accumulated in surface water to dangerous levels. Various algae were not equally effective in absorbing pesticides. Scenedesmus was particularly effective, and pH of the medium proved important with 2,4-D. In general, submersed species were not as effective in herbicide removal as emersed species. This may be related to transpiration and an avenue for translocation through the xylem. Herbicides were metabolized by various aquatic plants. Metabolites as well as the herbicide were released to the culture medium. Metabolism was rapid, particularly with algae. Algae and aquatic vascular plants contribute to the removal of pesticides from water and metabolize the chemicals to less active compounds in the environment.
Hybrid PWM control Scheme for Open-End Winding Five-Phase Induction Motor Drive to Suppress Lower Order Harmonics

High power industrial applications utilize inherently low switching inverters. As a result of this operation, the induction motor drive experiences derating due to the injection of lower order harmonics. A six step three phase inverter driven induction motors have not only the harmonic losses but also the bearing leakge currents due to common mode voltage. This paper proposes the hybrid PWM (slow switching) inverter driven open ended five phase induction motor with harmonic suppression. The degree of freedom is explored to counter the harmonics injected by the slow switched inverter by adding another high frequency switched inverter to compensate the harmonics.The results provided over a simulation and experiment confirms the mentioned advantages.
Whale Optimization Algorithm Combined with Oversampling Technique for Imbalanced Issues

Many sampling-based data preprocessing strategies are proposed to resolve imbalanced problems. The essential principle of sampling is to generate minority samples or filter majority samples based on a raw unbalanced dataset and finally get a rebalanced dataset. Combining oversampling methods with whale optimization algorithm for imbalanced issues will enhance the performance of existing oversampling algorithms. WOA-Oversampling consists of two main steps: reconcile train set with a specific oversampling technique and find the best set from the balanced train set using WOA. The biggest advantage of the algorithm is that it will make full use of existing oversampling methods and procure perfect train set. Experiments conducted on 10 imbalanced datasets compared with four existing overs ampling methods demonstrate the effectiveness of the algorithm.
Research on Crop Pest and Disease Diagnosis Method Based on Improved Faster R-CNN

Crop diseases and pests pose a significant threat to global food production. To effectively identify and combat these threats, this article introduces a method for intelligent detection using computer vision technology. Given the inadequacy of existing datasets for real-world applications, this study emphasizes the application of deformable convolution. This convolution technique enhances performance in complex spatial deformations by introducing offsets for sampling points of each convolutional kernel. Alongside the Faster R-CNN framework, we present a comprehensive intelligent detection workflow, elaborating from data collection and preprocessing to model evaluation and final disease detection. Experimental results indicate the proposed method can effectively detect and distinguish different crop diseases.
Research on multi-coils wireless power transfer System Based on Insulator with Flanges

This article claims the properties and working characteristics of multi-relay WPT systems, and studies various factors that affect the output efficiency of the system. At the same time, this article also investigates the impact of composite insulator metal flanges on the multi-relay WPT system when it operates in 110kV composite insulators, providing some guidance for the arrangement of all the coils in the composite insulator.
Toward Accurate and Real-Time Binocular Vision: A Lightweight Stereo Matching Network for RGB Stereo Images

The existing stereo matching networks with high accuracy generally demand high-computational overhead, making them expensive to deploy and unsuitable for many real-time applications, especially on source-constrained hardware platforms. Some recent works have tried to build lightweight stereo matching networks, but their accuracy and inference speed are far from satisfactory when deployed on source-constrained graphics processing units (GPUs). This article aims to build a lightweight stereo matching network with high accuracy. First, we claim that a reasonable disparity upsampling strategy is crucial for lightweight stereo matching to achieve high accuracy, whose importance is underestimated in the existing works. Accordingly, an efficient and effective disparity upsampling strategy named spatial adaptive disparity shuffle (SADS) is proposed to meet the upsampling requirements of disparities within diverse regions. Second, a novel channel-disparity-mixed attention (CDMA) mechanism is proposed to regularize the high-resolution 4-D compact cost volume. The attention values in CDMA are adaptive across channels, pixels, and disparity candidates, making CDMA suitable for regularizing the compact cost volume. Based on the proposed methods, a lightweight stereo matching network named SADSNet is designed that achieves real-time performance with high accuracy on source-limited hardware platforms (e.g., the NVIDIA Jetson TX2). In addition, SADSNet can be easily scaled up or down to suit different application scenarios according to available computational power. Extensive experimental results on multiple datasets show its clear superiority over state-of-the-art (SOTA) lightweight stereo models and even many large accuracy-oriented models.