Miracle-dz/UniFAR-FTUs huggingface dataset:
Formula for Calculating Sending-end and Receiving-end Voltages of AC Two-bus System with Constant Power Load P+ jQ

For calculation of voltages of an AC two-bus system, there exist conventional methods such as Ohmic calculation, Equivalent resistance method and etc. This paper presents the formulas for calculating the sending-end voltage E and receiving-end voltage V for the AC two-bus system with a constant power load P+jQ connected via transmission line admittance $\dot{\mathrm{y}}$=G-jB. The V-P equation(voltage-power equation) composed of voltages E,V and load power P,Q has been presented in a recent publication, from which the formula for sending-end voltage E can be derived with ease when V is given. However, the receiving-end voltage V when solved by Ohmic calculation or $V-P$ equation, becomes a $4^{\mathrm{t}\mathrm{h}}$-order equation w.r. t. V. We have to solve the $4^{\mathrm{t}\mathrm{h}_{-}}$ order equation and must select one practical solution out of four mathematical roots. An explicit formula for calculating the receiving-end voltage V is proposed in this paper by converting the V-P equation into the $2^{\mathrm{n}\mathrm{d}}-$order equation w.r. t. $\mu$, under the assumption that the system is on voltage-stable operation. Every existing method has its own multi-steps of solving process for obtaining the solution for voltage E or V, however, the proposed formulas need no such process. A unique solution can be obtained by simply substituting the given data into the proposed formulas. Example systems are solved by existing methods Ohmic calculation and V-P equation - and by the proposed formulas to compare the solving processes and calculation results with each other.
Synthesis of TPU/MWCNT Nanocomposite-Based Filament for Strain Sensors Application

There is a huge demand for flexible strain sensors due to the rapid development of wearable devices. Nevertheless, conventional metals and rigid semiconductor-based strain sensors have been limited in their application due to their poor stretchability and sensitivity, and a great deal of effort has been put into finding suitable sensors to overcome these limitations. Filament-based strain sensors are a new technology that can be used to make wearable electronic devices. Due to their intrinsic properties, filament-based sensors are superior to conventional rigid sensors for detecting human body motions due to their weight, flexibility, stitchability, and especially their high stretchability. In this paper, multiwalled carbon nanotubes and thermoplastic polyurethane (TPU/MWCNT) were combined using mechanical force methods to produce dispersions of MWCNT with different types of TPU. Different types of filaments were fabricated using an easy and cost-effective method, which is extrusion. Moreover, the effect of the nature of the TPU on the stability of the sensors’ conductivities is investigated. The performances of these filaments were analyzed in terms of mechanical, electrical, and electromechanical properties. The results show that the sensor with the combination of TPU3/MWCNT exhibits a strong capacity to transform mechanical deformation into an electrical response. The elastic zone was estimated at 24.49% (linearity = 0.98 within the elastic zone) and 10.1 MPa of young modulus.
Design of Intelligent Glass Cleaning Robot Measurement and Control System Under Machine Vision

Currently, glass curtain walls are commonly used on the exterior walls of high-rise buildings, but their cleaning is still in the manual cleaning stage. The cleaning of glass curtain walls relying on machine vision technology is an urgent problem. This article uses a monocular ranging algorithm to convert RGB (Red, Green, Blue) color space model images into HSV (Hue, Saturation, Value) color space model images, and achieves detection of window frames and glass. To verify the simulation results, this article explores the response time of the intelligent glass cleaning robot (IGCR) measurement and control system, the autonomous obstacle avoidance of the robot, and the angle changes under the condition of no external interference. When there is no external interference, the variation of the IGCR around the X and Y axes is less than $\pm 1^{\circ}$. The displacement change of the IGCR around the Z-axis is less than $\pm 3^{\circ}$, which meets the design requirements.
Predicting Metabolic Syndrome Risks Using Machine Learning Methods

This paper presents a comprehensive study on the application of machine learning (ML) methods for predicting the risks of Metabolic Syndrome (MetS). MetS, a complex disorder characterized by a cluster of metabolic abnormalities, poses significant health challenges worldwide. Leveraging a diverse dataset comprising clinical, genetic, and lifestyle factors, various ML algorithms were employed to develop predictive models for identifying individuals at risk of MetS. The following ML methods were used in this research: logistic regression, multi-layer perceptron neural network, Naive Bayes, J48, random forest, LogitBoost and BayesNet within a software tool Waikato Environment for Knowledge Analysis (WEKA). This study evaluates the performance of these models in terms of accuracy, precision and compares their predictive capabilities. Through multiple experimentation and feature importance analysis, we provide insights into the key factors contributing to MetS prediction. The final results showed that the proposed model achieves an accuracy of 92.38% in relation to the male population and 80.95% in relation to the female population. The results highlight the potential of ML techniques in early risk assessment for MetS. Also, it is important to note that ML cannot replace medical diagnoses, but it can help with the final assessment.
Technical note: Evaluation of three machine learning models for surface ocean CO 2 mapping

Abstract. Reconstructing surface ocean CO2 from scarce measurements plays an important role in estimating oceanic CO2 uptake. There are varying degrees of differences among the 14 models included in the Surface Ocean CO2 Mapping (SOCOM) inter-comparison initiative, in which five models used neural networks. This investigation evaluates two neural networks used in SOCOM, self-organizing maps and feedforward neural networks, and introduces a machine learning model called a support vector machine for ocean CO2 mapping. The technique note provides a practical guide to selecting the models.
Intelligent Practical Teaching Platform based on Data Mining

In the era of rapid Internet expansion, the data generated has witnessed exponential growth, concealing valuable information within. Consequently, the field of data mining has flourished, finding extensive applications across diverse industries. However, its full potential remains untapped in the realm of higher education. In light of the "Big Data Analysis and Mining" course, this paper makes a detailed investigation on the problems existing in practical teaching. To address these problems, we propose an intelligent practical teaching platform based on data mining technology. This platform harnesses data mining and cloud storage techniques, adopting a blended approach that seamlessly integrates online and offline teaching methods. It offers a comprehensive solution for practical teaching, with the primary objective of enhancing the effectiveness of practical teaching and augmenting students’ hands-on capabilities. Finally, we present the outcomes and applications of this platform since its inception over one year ago, showcasing its tangible impact on higher education.
Channel Sounder in Indoor Environment with Multipath Fading using Software Defined Radio

In wireless communication systems, it is essential to properly describe the radio channel to improve performance and ensure reliable transmission. This paper thoroughly examines how Software Defined Radio (SDR) technology was used to build and apply a channel sounder in an indoor setting. The goal is to record the features of multipath fading and figure out how different indoor propagation effects affect wireless communication systems. The proposed channel sounder takes advantage of SDR’s flexibility and ability to be programmed to send and receive signals efficiently and to use advanced signal processing methods to estimate the channel. By taking advantage of the broad nature of the signal being transmitted, the channel sounder can record the time-varying impulse response of the channel, including the multipath components and fading that comes with them. The results show that the channel sounder can correctly describe the indoor radio channel and give important information about how to design and improve wireless communication systems inside. We can capture many parameters in wireless communication, such as spectrum frequency, time domain analysis, constellation diagram, CCDF, Bandwidth Occupied, and Distortion measurement.
Road Traffic Prediction based on Multi-Feature BP Neural Networks

In smart transportation system, the traffic flow is one of the important metrics to mature road traffic. Along with increasing urbanization, road traffic becomes a thorny issue to limit the development of a city. As a result of technological advancements, the transportation system is enriched with smarter devices that help it perform more efficiently and effectively. One efficient approach is leveraging existing data to predict the road traffic flow, in order to optimize road usage efficiency. There are many studies to leverage existing data to predict road traffic flow. However, the limitation is the generic traffic model cannot represent real dynamic traffic flow precisely. In this study, we analyze macroscopic traffic flow and traffic density, as well as microscopic speed, acceleration, and distance for each individual vehicle in the smart transportation context. Based on the analysis, we propose a macroscopic traffic flow model. Focus on the limitation of existing research, we also involve lane switching in the model and propose the lane switching decision making strategy. After we optimize the traffic model, we adopt BP neural network to predict the road traffic flow. The evaluation results show that our multi-feature traffic model can increase the prediction accuracy on real road traffic datasets.
Optimal Operation Study of Distribution Network Based on Flexible Multi-State Switch and Shared Energy Storage

The distribution network requires additional flexibility to cope with the large-scale integration of distributed energy sources. Energy Storage Systems (ESS) can smooth the fluctuating output of renewable energy. However, due to high investment and maintenance costs, equipping multiple ESS units within a single system is not practical. To address these challenges, this paper proposes a distribution network optimization scheme based on a shared energy storage strategy.In this scheme, a Flexible Multi-state Switch (FMSS) with integrated energy storage systems is introduced at the endpoints of the distribution network. This enables the sharing of charge and discharge energy between nodes. The optimization of distribution network voltage operation is modeled as a Markov Decision Process (MDP) framework. Additionally, a Q-depth neural network algorithm is proposed to effectively address the issue of high-dimensional decision variables, aiming to determine the optimal charging and discharging actions of battery energy storage systems. The proposed approach is validated using an enhanced IEEE 33-node system as a case study, demonstrating the method’s robustness and effectiveness in reducing power losses and mitigating voltage fluctuations.
Food Classification using Deep Learning Techniques

Image recognition of real-world food plays an important role in the vast scope of applications. For example, food classification can estimate the gradients of any type of food to help diabetic and ill people to follow a healthy diet. Therefore, researchers have proposed methods and algorithms to recognize different types of food. However, the recognition process is a very challenging task due to the unlimited variations within the captured images of different kinds of food. Hence, we propose a new dataset which contains 20000 images of 15 different classes of local meals. A semi-automatic method is presented to provide the data annotations. The collected images are used to propose detector and recognizer models. The trained detector provides a detection rate of 81%. The train detector is designed by adopting YOLO v3-tiny. Additionally, the food classification process is performed based on two types of features: traditional features and learned ones. The results show the superiority of the deep learning features over the hand-craft features. Adopting the transfer learning techniques with the pre-trained model of Resnet50 achieves about 95% accuracy of classification rate.
A Comprehensive Performance Space Comparison of Bandgap Reference Circuits Through Hard Constrained Analog Circuit Multi-Objective Optimization

As the demand for ICs continues to surge, there is an imperative need to expedite the circuit design process. Addressing this challenge has led to the growing popularity of automation and optimization tools. Nevertheless, the optimization of nonlinear circuits, such as BGRs, presents inherent complexities. This paper presents a detailed comparison of two different circuit BGR topologies (CMOS-only and Banba’s BGR) through multi-objective optimization while adhering to identical and hard constraints. Through this approach, a rigorous comparison is conducted between these two circuits, extending beyond the scope of a single design to encompass the entire design space. This comprehensive analysis provides valuable insights into the performance of these circuit topologies. The results reveal that a CMOS-only circuit topology outperformed Banba’s BGR in terms of temperature variations, power consumption, and noise performance. On the other hand, Banba’s BGR exhibits better performance in line sensitivity and Power Supply Rejection (PSR). To our best knowledge, this paper stands as the first endeavor to thoroughly investigate and draw a comprehensive performance comparison between CMOS-only and Banba’s BGR.
Stroke Rehabilitation Action Recognition Based on MY_SlowFast

In order to solve the problem of low model operation efficiency caused by the increase in the number of parameters and calculations of the spatio-temporal action recognition model when improving the accuracy of prediction targets, a lightweight spatio-temporal action recognition network MY_SlowFast was designed based on the SlowFast model. The YOLOv8 network with high real-time and high accuracy was selected to replace the original target detection network Faster RCNN; secondly, the lightweight MobileNetV3 network was selected to rebuild the backbone network of YOLOv8 to maintain feature richness while accelerating feature extraction speed and achieving model lightweighting. Experimental results show that compared with the original YOLOv8 model, the improved lightweight target detection model reduces the number of model parameters and calculations by 77.8% and 77.7% respectively. Compared with the original Slowfast model, the improved MY_SlowFast model improves the average detection time by 1.4 seconds per frame.
A Regression Mixture Model to understand the effect of the Covid-19 pandemic on Public Transport Ridership

The Covid-19 pandemic drastically changed urban mobility, both during the height of the pandemic with government lockdowns, but also in the longer term with the adoption of working-from-home policies. To understand its effects on rail public transport ridership, we propose a dedicated Regression Mixture Model able to perform both the clustering of public transport stations and the segmentation of time periods, while ignoring variations due to additional variables such as the official lockdowns or non-working days. Each cluster is thus defined by a series of segments in which the effect of the exogenous variables is constant. As each segment within a cluster has its own regression coefficients to model the impact of the covariates, we analyze how these coefficients evolve to understand the changes in the cluster. We present the regression mixture model and the parameter estimation using the EM algorithm, before demonstrating the benefits of the model on both simulated and real data. Thanks to a five-year dataset of the ridership in the Paris public transport system, we analyze the impact of the pandemic, not only in terms of the number of travelers but also on the weekly commute. We further analyze the specific changes that the pandemic caused inside each cluster.
On the 4D Mixed Lattice IIR Digital Filter: Part I

This study is concerned with a new four dimensional (4D) lattice filter which has the special feature that the orientation of the internal arrows has mixed directions (right and left). This filter, utilizes a minimum number of time delays as well as an overall minimum statespace vector. Moreover, the all–pass lattice property, as in the conventional one–dimensional (1D) case, also applies to the proposed four–dimensional mixed lattice filter. Low–order four and two dimensional examples are used to illustrate the properties and structures of the mixed lattice circuit implementations.
Hyperbolic Node Structural Role Embedding

Hyperbolic space has been shown to be able to naturally reflect the properties of complex networks that exhibit hierarchical structure. This has led to the development of a number of hyperbolic network representation learning methods which have been shown to be on average superior to the more traditional network representation learning in Euclidean space. The main focus of existing hyperbolic network embedding methods has been local (i.e., microscopic) node embedding. That is, learning node embeddings based on their local neighborhood. Work on hyperbolic network embedding has so far not investigated hyperbolic node structural role embedding (i.e., macroscopic embedding). That is, learning node embeddings based on their structural roles.In this work, we attempt to address this gap by extending two commonly used methods used for Euclidean structural role embedding–Random walk and Matrix factorization–to perform structural role embedding in hyperbolic space. Specifically, we show how Euclidean structural role embeddings methods utilizing these methods can be moved into hyperbolic space. Experiments on several real-world and synthetic networks show that our structural role embedding methods in hyperbolic space achieve better results than their Euclidean counterparts, with one of our methods outperforming the current state-of-the-art. Our results add further support to the growing body of literature that show that hyperbolic space is more effective than Euclidean space for graph representation learning, specifically in our case, node structural role representations.
Enhancing the Spectral and Power Efficiencies by Combining MIMO and OFDM-SPM Schemes

The demand for new technology to meet the requirements of the new generations such as 5G and 6G has increased. Significant efforts have been made by developers around the world to investigate a new technology to fulfill the requirements of the aforementioned generations, in terms of high data rate, high reliability, and little complexity. A new technology called "orthogonal frequency division multiplexing with subcarrier power modulation" (OFDM-SPM) has been studied in this paper, while combined with the "multiple-input multiple-output" (MIMO) system. This combination of the two wireless technologies provides more advantages and features, such as double spectral efficiency (SE), high-energy efficiency (EE), reduced transmission delay, and reduced complexity of the system. However, the system shows a degradation in the bit error rate (BER) as compared with the classical MIMO-OFDM system. In this paper, the system performance will be enhanced through power reallocation policies (PRPs) by utilizing the saved power of the system to improve the system efficiencies. The simulation results show an improvement in the BER and enhancement in the system throughput performance, making the system more suitable for future wireless applications.
Dual-wavelength-modulation mm-wave system based on single-sideband signals

Due to their simplicity, dual-wavelength-modulation (dual-λ-mod) photonic transmitters are an attractive alternative to conventional heterodyne systems for mm-wave/THz signal generation. In contrast to what has been reported in the literature, the authors of this manuscript believe that dual-λ-mod systems based on DSB-C modulation are subjected to power fading, rendering them useless for high-speed mm-wave/THz communications. To solve this, we propose a dual-λ-mod system based on SSB-C modulation. Using a GS laser, a 60-GHz SSB-C dual-λ-mod system transmitting 2.5-GBd 16-QAM signals is demonstrated. A PD-input-power penalty of just 2.8 dB is measured with respect to the conventional heterodyne transmitter at a BER of 10−3 for optical B2B and 10 km fiber transmission.
Financial Trading via Reinforcement Learning and Convolutional Neural Networks

The paper presents a novel approach to developing a pair trading strategy for cryptocurrencies by employing a customized Rainbow DQN and an image encoding technique. This method transforms candlestick features of Bitcoin (BTC) and Ethereum (ETH) into images, which are then input into a Convolutional Neural Network (CNN) for feature extraction before being fed into the Rainbow DQN model for making trading decisions. Although the Rainbow DQN strategy outperforms RSI and correlation strategies, none of them are profitable after factoring in transaction costs.
Compact Optical 90° Hybrid based on a Wedge-shaped 2 × 4 MMI Coupler and a 2 × 2 MMI Coupler on Thin-film Lithium Niobate Platform

We conducted an experimental demonstration of a wedge-shaped optical 90-degree hybrid coupler on the thin film lithium niobate (TFLN) platform, utilizing a paired-interference-based 2 × 4 multimode interference (MMI) coupler and a general-interference-based 2 × 2 MMI coupler. The fabricated optical 90-degree hybrid coupler has a compact footprint with a length of 134 μm. In a coherent receiving system, the demonstrated hybrid coupler directly connects to the balanced photodiode array without waveguide crossing or cascaded phase shifters. The device exhibits an excess loss of less than 1.1 dB and a phase deviation of less than ±5 degree over a spectral range of 1530nm-1560nm, which is promising to enable a compact heterogeneously integrated coherent receiving system on the thin film lithium niobate platform.
93 GHz Wireless Transmission based on a Fully Packaged mm-Wave Band Optical Clock Generator

The mm-wave frequency spectrum offers the possibility to afford the increasing demand of high data-rate communication links. Microwave-photonics techniques can be advantageously used for reconfigurable generation of very high frequency clocks with superior phase noise performance with respect to purely electrical techniques, with the additional functionality of remote distribution of the signal through fiber-optics or free-space optic links. Here we present a fully packaged optoelectronic assembly of a CMOS-compatible silicon-based photonic integrated circuit for optical frequency comb generation with reconfigurable free spectral range given by an RF driving local oscillator. Through optical filtering and photodetection, a 98 GHz electrical clock has been derived and employed for successful 93 GHz wireless transmission of complex modulation formats carrying up to 4 Gb/s data rate, demonstrating the suitability of the solution for 5G and next generation 6G mobile networks.