Miracle-dz/UniFAR-FTUs huggingface dataset:
AI Based Novel Feature Engineering using ANN Applied on Women SES Classification

Feature engineering is an important step in data preprocessing which helps to identify and retain only the significant features which contribute to the model learning from among large features present in the raw dataset. This reduces the unnecessary burden on the Machine Larning (ML) or Artificial Intelligence (AI) model resulting in speedy learning and improved performance efficiency. In this research, a novel AI model for feature engineering is proposed using Artificial Neural Network (ANN). A new algorithm for AI based feature engineering is developed. The developed model is applied and on the raw dataset obtained to classify the socio-economic status of women consisting of thirty features. The proposed feature engineering algorithm is applied by dividing the features into two halves and running on two different ANNs. The significance of the feature is determined by the value generated. The error values are sorted and features with the least error contribution are dropped which indicates its least significance in the dataset. The newly developed feature engineering model is very effective in classification of SES into low medium and high with 98.52% accuracy. The model is benchmarked with other public datasets and performed with a high learning rate. The model is also evaluated and validated with other machine learning algorithms and proved to be very effective in increasing the learning rate and model efficiency.
Research on Infrared Wide-Spectrum Inversion of Long Range Targets Based on Radiation Transfer Model

In the case of long-distance detection, the information obtained by spectral detection equipment will be subject to atmospheric scattering, transmittance, and optical system attenuation, resulting in a certain deviation in the output of the detection equipment. Therefore, based on a large amount of data and combined with target radiation calibration technology, this article establishes a system radiation energy conversion and correction model by obtaining multiple sets of different gain response functions and radiation biases of the equipment through a large amount of calibration data.
Identification of Mood in Early Childhood with Face Recognition

There are many factors that affect the mood of children during the teaching and learning process in class. Some of the causes of children having a bad mood are that the child is still sleepy when going to school, getting unpleasant treatment, an uncomfortable environment at home, and less attention in the classroom due to the teacher’s limitations in monitoring students during learning. At the same time, a good mood is imperative to maintain concentration and enthusiasm in the learning process in class. This research proposes the Convolutional Neural Network (CNN) method, which is also one of the methods in Deep Learning. CNN can receive input in the form of images which machines can then use to learn to recognize images, determine what objects or aspects are contained in the image, and so on. The moods to be identified consist of angry, neutral, sad, and happy, with a total of 26,215 FER2013 data. Several metrics are used to evaluate the model’s performance, such as Accuracy, Precision 79%, Recall 50%, and F1-score 61%. CNN performance in the classroom for early childhood mood identification achieved 84% accuracy. The system created in this study works by detecting human objects through videos taken using a camera, after which the face recognition stage is carried out then feature extraction is carried out, then classified according to the available data model. The last stage is to save the results of mood identification in the form of a report so that it can be accessed at any time.
A SIW multi-polarized leaky-wave antenna based on multi-slot structure

With the development of MIMO(multiple-input multiple-output) technology, multi-polarized antennas are more and more widely used in communication systems, and various types of SIW(substrate integrated waveguide) multi-polarized leaky-wave antennas emerge in endlessly. In order to reduce cell spacing and the size of leaky-wave antenna, a multi-slot cell structure with high dispersion characteristics is designed based on rectangular slot. The multi-slot structure can suppress the open stopband to a certain extent and realize the continuous beam scanning from backward to forward. A multi-polarized leaky-wave antenna is composed of two 45° multi-slot linear arrays and a 3dB coupler, which can realize four polarization modes: left-handed circular polarization, right-handed circular polarization, -45° polarization and +45° polarization. The measured results show that the beam scanning range is about 60° and antenna gain is more than 9dBi regardless of circular polarization or linear polarization.
Study on Scattering Characteristics of Raindrop Particles at Multi-bands for Weather Radar Detection

In this paper, the scattering characteristics of raindrop particles at four bands used for weather radar detection are studied. Single raindrop particles are established at four frequencies which are in S, C, X and Ku bands. The radar cross sections (RCSs) are simulated and compared with the calculated theoretical results. It can be found that these two results are consistent, and some values are slightly different. Meanwhile, the scattering characteristics of randomly distributed raindrop particle swarms are also researched at the same four frequencies. The theoretical equation does not include multiple scattering, but the full-wave simulation includes it. Therefore, the trends of the theoretical and full-wave simulation results are consistent, but the difference is larger. It proves that multiple scattering cannot be neglected.
A Low-Profile UWB Wide-Angle Scanning Phased Array

With the continuous improvement of radar integration, reducing antenna profile has become an increasing demand. Traditionally, with the increase of phased array bandwidth and scanning angle, the profile height of impedance converter and the number of wide-angle scanning matching layers of frequency selective surface (FSS) are also increasing, which makes the overall antenna profile constantly increased. Therefore, it has been a difficult point to design a low profile ultra-wideband (UWB) wide-angle scanning phased array. Based on this, a new low-profile UWB wide-angle scanning phased array is designed in this paper, by adding a newly designed resistance card (R-Card) and changing the linear feed line of the impedance converter to a curved one. Under the premise of keeping a single layer of FSS wide-angle scanning matching layer, the scanning angles of ±70° in plane E and plane H are maintained with the VSWR < 2.5, the profile height is 0. 1λlow, where λlow is the wavelength of the lowest working frequency, and the array working frequency is 2.9-15.7GHz (5.4:1).
A Circularly-Polarized and Mode Reconfigurable OAM Shared-Aperture Hexagonal Antenna Array

A K-band dual circularly polarized (CP) and multiple mode reconfigurable orbital angular momentum (OAM) hexagonal antenna array (HAA) is presented. The proposed HAA consists of twelve dual CP horn antenna elements. By controlling the port input phase of each horn antenna element, the OAM modes generated by the proposed HAA can be reconfigured between l = 0, ±1, ±2, and ±3 for both left-hand CP (LHCP) and right-hand CP (RHCP). Moreover, the LHCP and RHCP modes can operate simultaneously under the same radiation aperture. The simulated results verified the effectiveness of the HAA.
Isolation Enhancement of a Compact Antenna System for Detection of Target Information

The proposed antenna system outlined in this letter offers a streamlined and efficient solution for detecting target information across a frequency range of 15.7 GHz to 16.7 GHz, featuring both high isolation and enhanced gain. The suggested setup of the antenna system includes a sender antenna, a noise reducing amplifier, and a receiver antenna. The DGS applied to the ground structure has resulted in reduced mutual coupling between the transmitting and receiving antennas, while the low noise amplifier has significantly improved the gain of the system, both of which are possible due to the co-polarization of the antennas. The prototype antenna system has been fabricated and tested to confirm the validity of the proposed design. The obtained data from the experiment closely aligns with the predicted outcomes, indicating minimal interference with a signal-to-noise ratio exceeding 22.26 dB, a maximum amplification of 15.09 dBi across the designated frequency range, and consistent and balanced radiation properties.
A Hybrid Extractive-Abstractive Framework with Pre & Post-Processing Techniques To Enhance Text Summarization

The goal of this paper is to enhance text summarization using a hybrid methodology. The process of producing a condensed version of a text while keeping its essential details is known as text summarization. In this study, we have presented a method for training the T5 model on the SAMSum dataset with conversation sentences to increase its effectiveness in text summarization. To determine the impact of training the T5 model on the dataset, the model is assessed using the ROUGE metric both before and after training. ROUGE is a set of metrics used to evaluate the quality of automatic summaries by comparing them to reference summaries based on the overlap of n-grams, word sequences, and other linguistic units. In order to enhance the quality of the generated summary, our hybrid approach makes use of extractive and abstractive summarization techniques as well as pre-and post-processing techniques. There is an improvement in the ROUGE metrics of the model before and after training. ROUGE1 before training was observed to be 25.53 while ROUGE1 after training is calculated to be 45.17.
Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners Under Networking Constraints

Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are not only heterogeneous but also not fully generated locally. In this paper, we propose an experimental design network paradigm, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners, and the decision variables are the data transmission strategies subject to network constraints. We first show that, assuming Poisson data streams in steady state, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a $1-1/e$ factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling. Finally, we complement our theoretical findings through extensive experiments. Our numerical evaluation shows that the proposed algorithm outperforms several baseline algorithms both in maximizing the global objective and in the quality of the trained models.
Investigation of Ultra-Wideband Antenna Array Based on Wavelength-Scaled Array Architecture

The wavelength-scaled array architecture use scaled elements to achieve ultra-wideband array aperture with fewer overall radiating elements, compared to traditional uniform dense array. As a consequence, the wavelength-scaled array reduces element count and associated T/R module, which will decrease the cost and weight of the electronic equipment. A nine elements linear E-Plane array based on WSA layout is designed and simulated. The simulation results show that the impedance bandwidth is 2.5~10GHz, with a simulation peak gain of 14.2 dBi at 7GHz. The proposed antenna array based on WSA architecture is a promising method for low cost UWB antenna array.
Decoding the Mind's Symphony: A Mathematical Exploration of Mindfulness Meditation and Visual Working Memory

The main aim of this study was to investigate the effects of mindfulness meditation training on information processing during a working memory task involving challenging visual stimuli. Participants, consisting of a meditation group and a matched control group, underwent an intensive one-month mindfulness meditation program. The working memory task, specifically a delayed face recognition task, was administered both before and after the training period. response time (RT), RT variability, and accuracy were measured as performance indicators. To gain insights into the underlying mechanisms, mathematical modeling techniques utilizing the EZ Diffusion (EZD) and Noisy Exemplar models (NEM) were employed. The application of the EZD model revealed noteworthy findings for the meditation group, including an increase in drift rate (indicating improved perceptual information quality) and a decrease in the decision boundary. Furthermore, the Noisy Exemplar model analysis demonstrated a decrease in encoding noise (indicating reduced attentional lapses) for the meditation group. These results suggest that mindfulness meditation training enhances working memory performance by boosting attention and perceptual information quality. By combining behavioral tasks and mathematical modeling approaches, this study offers a comprehensive understanding of the cognitive effects of mindfulness meditation. The findings highlight the potential of mindfulness training to improve attentional processes and enhance working memory.
The Design of a 41.6GHz~62.5GHz All-Solid-State Source Module with High Power Flatness

In this article, a 41.6GHz~62.5GHz quadruple source module is presented. The 2 stages of doubler and several filters as well as amplifiers are used to generate sufficient output power. Due to the output power of the last stage amplifier continues to decline at high frequency, a new kind of positive slope equalizer featured with resistive film is introduced to adjust it. Finally, the measured results of the source module show that from 41.6GHz to 62.5GHz, the minimum output power is 23dBm; meanwhile, the power flatness and the harmonic suppression are better than ±0.5dB and 40dBc, respectively.
Mirror-like Defect Detection Technology based on YOLOv5 and Phase Measuring Deflectometry

As deep learning is used to collect and analyze the defect information of mirror-like objects, the original two-dimensional image can be easily affected by the illumination environment, thus causing the defect features difficult to be recognized and extracted by the neural network. In order to improve the detection efficiency, this paper proposed a method that combines deep learning and PMD (Phase Measuring Deflectometry), using the YOLOv5 network to detect multi-feature composite images. The self-built data set was marked with 7 defect types. The test results showed that the recognition rate of this method could reach more than 90% for the same test set. The detection speed is 7.4 ms/frame, and the detection performance is much better than that of a single deep learning method.
Hardware and software of a 3D printed humanoid hand

Creating low-cost and completely functional humanoid hand that meets individual needs and applications in prosthetics or robotics is still a challenge. This paper presents the development of an innovative 3D printed humanoid hand with modular fingers. This approach makes it possible to reduce the cost by taking advantage of the creation of complex shapes and individual sizes of the fingers. The software and hardware implementation of the modules is described. A control system has been designed that allows adjustment and control of the positions of fingers of different sizes and joint constraints. A 3D printed prototype is created. The hardware and software are tested. Main functional capabilities of the hand are studied.
Robust Visual SLAM in Dynamic Environments Using Feature Matching and Regularization

In this paper, we consider dynamic visual SLAM based on the feature matching and regularization. Currently, most of the visual SLAM approach is based on the assumption of a static world. In an ideal environment, these methods can achieve good work results, but when dynamic objects appear in the camera field of view, system performance is degraded or even work fails. On the basis of ORB-SLAM2 [2], we design a static map point detection strategy to ensure that only static points are used for pose estimation. Subsequently, in order to further improve the adequacy and accuracy of static map point recognition, we adopted a map point regularization strategy to optimize the dynamic and static labels. Finally, experimental results on the TUM dataset show that our method achieves high accuracy while ensuring real-time performance under a CPU computer.
Research on the Inﬂuence of Accounting Conservatism on Enterprise Innovation

This paper takes China’s A-share non-ﬁnancial listed companies from 2011to2021asresearchsamplestoexploretheimpactofaccountingconservatismonenterpriseinnovation,studiestheimpactofpropertyrightsnatureontherela-tionshipbetweenaccountingconservatismandenterpriseinnovation.Theresults showthattheimprovementofaccountingconservatismwillrestrainthelevelof innovationinputandinnovationoutput.Comparedwithstate-ownedenterprises, accountingconservatismhasamoresigniﬁcantinhibitoryeffectoninnovation inputandoutputinnon-state-ownedenterprises.
A 0.48-THz Fully-Differential FMCW Radar Transceiver in 90-nm SiGe BiCMOS

A 0.48-THz fully-differential quasi-monostatic frequency-modulated continuous-wave (FMCW) radar transceiver was designed in a 90-nm SiGe bipolar CMOS technology with $f_{T}/f_{\max}$ of 300/480 GHz, occupying 2.84 m$\mathrm{m}^{2}$ of chip area. The signal generation at 0.48 THz is accomplished using a multiply-by-24 multiplier chain, with two 0.48-THz push-push doublers driven in quadrature for a differential output signal. The differential subharmonic receiver mixer uses a stacked quad topology with measurements revealing a system conversion gain of 7 dB and a single-sideband noise figure of 25.2 dB at 0.471 THz as well as an effective isotropic radiated power of up to -8.8dBm at the transmitter at 0.47 THz. The transceiver is verified in an FMCW radar measurement using an on-board frequency source of a 5.5$\times$ 8.5c$\mathrm{m}^{2}$ demonstrator at a bandwidth of 60 GHz.
A Millimeter-Wave Radar Imaging Mode Combining Transmit Scanning and Receive Digital Beamforming

A millimeter-wave radar imaging mode combining transmit scanning and receive digital beamforming (TSCAN-RDBF) is proposed to solve the problems of strong target covering weak target and multipath interference in the practical application of MIMO mode. In the TSCAN-RDBF mode, spatial filtering is achieved by scanning the space in the transmission stage using a phased array. In the reception stage, the full high-resolution radar image is obtained by compressed sensing of the echo signal at the position of each transmit beam to achieve angle super-resolution within the transmit beam. The effect of MIMO mode and TSCAN-RDBF mode in weak object detection and indoor scene reconstruction are compared in the same millimeter-wave radar system. The experimental results show that the TSCAN-RDBF mode can efficiently detect weak targets with RCS of -82dBsm under interference from targets with RCS of 140dBsm. At the same time, the TSCAN-RDBF mode can effectively suppress multipath interference in the scene.
Depolarized L-Shaped Chipless RFID Sensor Tag for Soil Moisture Monitoring

A chipless radio frequency identification (RFID) tag is investigated for monitoring soil moisture. The proposed chipless tag is based on three sets of nested scatters with a small size of 75 mm × 75 mm × 1.6 mm, operating in the 0.5–2.6 GHz frequency band. The outermost scatterer is used to wirelessly monitor the volumetric water content (VWC) of the soil, with a VWC range of 5–30%. The inner two groups of scatters are designed for encoding with a capacity of 2 bits. Furthermore, the designed chipless sensor tag possesses depolarizing characteristics, enhancing the reliability of monitoring in a complex soil environment.
Wireless Power Transmission of Electric Vehicle

This paper presents a novel localization method for electric vehicles (EVs) charging through wireless power transmission (WPT) using magnetic resonant coils. In this study, a 2-coil WPT system's modelling, design, and impelmentation are presented for use in charging electric vehicles. Because the prototype system operates in a relatively low frequency range (less than 1MHz), it can be built for very little money using typical power electronic equipment. Based on the characteristics of the prototype, a circuit model was developed to determine the most effective operating range for the wireless power transfer (WPT) system. The model takes into account practical considerations and allows for an examination of the relationships between the excitation frequency, coupling, and output power based on circuit and magnetic principles. Experimental tests were conducted to validate these relationships and ensure the accuracy of the findings.
Factors Influencing the Use of Sports Live Streaming Video Services Based on the Hedonism Model

Numerous people were hurt and killed in the disaster at the Malang Kanjuruhan Stadium on October 1, 2022. Conversely, the usability of Video Live Streaming Services has been around for a long time. Recently, more people are turning to the internet and digital platforms to watch sporting events rather than the conventional way. It is the background of this research. Based on previous studies, the discussion of technology implemented to engage audiences, the Video Live Streaming Services, and real-time interaction usage models are used to see engagement from the audience. However, this research aims to gain more in-depth insights into the hedonistic side of a person’s nature and audiences’ interest in watching sports content using video streaming. For that reason, this research uses a quantitative method with Structural Equation Modeling - Partial Least Squares (SEM-PLS) to explore the influence factors of the usage of video live streaming in sports. A total of 351 respondents were collected for this study using Google Form. 273 respondents enjoyed watching content through video streaming, 220 respondents enjoyed watching video streaming for sports, but only 194 were valid through appropriate screening and outlier removal. The results of this study will be useful for the improvement of video streaming services and the development of the hedonism model.
MCUNet: Multidimensional cognition UNet for multi-class maize pest image segmentation

Corn leaf pest is one of the leading causes of poor quality and low yield of corn. In order to quickly and accurately identify the types and forms of corn leaf pests, this paper proposes a multi-classification corn pest segmentation network, Multidimensional Cognition UNet (MCUNet). This network can classify different pests and accurately segment the details of pest morphology. Firstly, a Nested Multi-scale Extraction (NMSE) module is designed to extract deeper dense multi-scale spatial information, enabling the network to use detailed contour information. Secondly, Multilayer Perceived Self Attention (MLPSA) is proposed to complement the long-range dependencies of feature vectors, allowing the network to better distinguish the apparent differences between target and background regions. Finally, for the multiclassification task, Squeeze-and-Excitation Networks were added to MLPSA to enhance the network’s ability to discriminate between different pests. The experimental data comes from a self-built dataset of corn pests. The experimental results show that the proposed network outperforms the current mainstream neural networks’ classification and segmentation performance.