Miracle-dz/UniFAR-FTUs huggingface dataset:
A Stretchable and Conformal 1×8 Magnetic dipole Array with Wide-Angle Scan Ability

A novel stretchable and conformal 1 × 8 magnetic dipole array with wide-angle scan ability is presented. The antenna array is composed of the traditional dielectric substrate, copper-clad polyimide, and flexible ecoflex, which is flexible while ensuring stable performance. Compact magnetic dipoles are used for the antenna element, which is conducive to the array stretching and conformal between the array and curved surfaces. The wide-angle scanning is realized by using the wide-beam characteristics of the magnetic current parallel to the ground. The proposed antenna operates at 5.8 GHz in the ISM band and has a matching bandwidth of 4.5% (5.65 -5.79 GHz). When the unit spacing is stretched from 0. 425λ0 to 0. 5λ0 after array formation, the array can scan from −67° to +68° and ± 60° within 3-dB fluctuation respectively. The array maintains ± 60° scanning angle when it is conformal with a cylindrical surface.
Broadband Measurement of Dielectric Properties for Low Loss Materials

In this paper, a test model based on the separated resonator method, the design of the separated resonator and the test results are presented, and the dielectric properties of thin plate materials in a wide frequency band are obtained. And it belongs to nondestructive testing[4]. The frequency range of the test can cover the millimeter wave frequency band. For low loss materials, good test accuracy can be obtained by using this test method. The measurement error of dielectric constant is less than 1%, and the measurement error of loss angle tangent is less than 0.0005.
Research on variable step size LMS algorithm based on exponential function

Least mean square algorithm, referred to as LMS algorithm, is an improved algorithm of steepest descent algorithm. It is an optimized extension of the Wiener filter theory after using the rapid descent method, which was first proposed by Widrow and Hoff. LMS algorithm has the characteristics of low computational complexity, good convergence in the environment where the signal is stationary, unbiased convergence of its expected value to the Wiener solution, and stationarity when the algorithm is implemented with finite precision, which make LMS algorithm the most stable and widely used algorithm among the adaptive algorithms. Since the fixed step size LMS algorithm can not achieve the unity of low steady-state error and fast convergence speed, this paper proposes a variable step size LMS algorithm based on exponential function. According to the properties of the exponential function, the nonlinear relationship between the step size and the error signal is established, and the step size factor is dynamically adjusted to meet the requirements of low steady-state error and fast convergence of the algorithm. This paper focuses on verifying the influence of the parameters α and β in the variable step size function on the performance of the algorithm, and compares the performance of several other new variable step size algorithms. Simulation results show that the proposed algorithm achieves a good balance between convergence speed and steady-state error. The research on adaptive algorithm is of great significance.
Research Challenges in Cervical Cancer Segmentation and Classification Using Colposcopy Images

Over the past three years in Indonesia, there have been 36,633 new cases of cervical cancer, the country’s second most common cause of death. Out of those cases, 21,003 people have died. The total number of deaths from cervical cancer in the past five years is 92,930. Potential deaths from cervical cancer can be prevented by early detection, such as pap smear, colposcopy, and biopsy. Experienced experts can only perform some of these measures, but the number of experts is currently 430 based on Indonesian Ministry of Health data. This study reviews several studies on cervical cancer detection using deep learning and machine learning models on colposcopy images. Since, there are still challenges to determine standard colposcopy images and overcome the interference of reflected light around the cervical lesion area. Therefore, this study will be helpful for researchers to consider practical and efficient pre-processing, feature extraction, segmentation, and classification.
SDN-Based LDoS Mitigation System

Software Defined Networking (SDN) is an emerging network architecture that features a separation of control plane and data plane, as well as strong centralized management capability and scalability. However, the centralized control mode of SDN also exposes unique vulnerabilities. When a Low-rate Denial of Service (LDoS) attack occurs in an SDN architecture, the entire network is also affected. Based on the feature of OpenFlow flow tables for classifying and statistics of each flow, this article proposes a SAX-DSW mitigation system that combines the PAA-SAX and DSW-DT algorithm to locate the attack source and victim port. A LDoS attack defense strategy is also designed that combines finding the attack port and blocking the attack path. To better demonstrate the functions and metrics of the attack detection and defense system, a Python-based Graphical User Interface (GUI) system is developed.
A Ka-band Metallic Luneburg Lens Antenna Based on Bed of Nails with Continuous Gradient

In this paper, a Ka-band fully metallic surface-wave Luneburg lens antenna is proposed. The antenna is composed of a metallic slow wave lens based on bed of nails with continuous gradient and nine standard WR-28 rectangular waveguides. The corrugated slots are designed at the edge of the lens for the modification of the beam direction in E-plane. The heights of nails are designed to match the equivalent refractive index distribution of Luneburg lens. The full-wave simulated results show reflection coefficients below −13.7 dB and a beam scanning range of ±60° at the band from 26.5 to 30 GHz. The sidelobe levels lower than −10 dB and maximum gain of 18.2 dBi are obtained.
A Wideband 8-Element MIMO Antenna for 5G Terminal Applications

A wideband multiple-input and multiple-output (MIMO) antenna with 8 elements is proposed in this paper. The eight antenna units are disposed symmetrically along the long side of the antenna. Every antenna element is composed of several L-shaped stub monopole and a slot structure, which achieves wideband characteristic covering 3.18GHz-5.65GHz. Moreover, two slots etched on the ground and four L-shaped monopoles are adopted to solve the mutual coupling of antenna elements, achieving isolation better than 13dB over the operating band. The proposed antenna has a total efficiency higher than 60% and envelope correction coefficients (ECCs) lower than 0.1. This makes it an excellent choice for fifth-generation(5G) terminal application.
Deep Learning-Enhanced Fingerprint Generation and Security Verification in the Context of Siamese Network Matching Models

Fingerprint recognition, a widely adopted technology in various domains, confronts significant challenges in both technical and practical realms. Therefore, it is essential to develop an effective algorithm to improve the accuracy of fingerprint recognition. In this study, a novel method is proposed to generate fake fingerprint images by Deep Convolution Generative Adversarial Networks (DCGAN) network, utilizing FVC2002 and FVC2004 as the dataset. Furthermore, a dataset consisting of 6, 000 fingerprint pairs, with the first 3, 000 pairs collected from the same individual and the remaining 3, 000 pairs from different individuals, is employed to train a Siamese Network. Finally, the real fingerprint image and the generated fingerprint image are used as two inputs to the Siamese Network to verify whether any two fingerprint images can be incorrectly matched. Experimental results indicate that DCGAN has an excellent ability to generate fingerprint images, although a small portion of the generated images have defects, which may be caused by the model training the blank part of the images as important features, etc. Additionally, the security verification experiment employing the Siamese Network reveals potential vulnerabilities in the fingerprint recognition system, possibly stemming from the network’s focus on localized similarities between the two input fingerprint images.
Wideband and Dual-polarized Metasurface Patch Antenna for FTTR Applications

Fiber to the room (FTTR) for the fifth-generation fixed network (F5G) systems greatly improves user’s internet experience. In this paper, a low-cost wideband, dual-polarized patch antenna is designed in low profile. To extend current high band of 5.8G in WLAN to the coming Wi-Fi 6E requirement, some metasurface structures are proposed around the conventional patch antenna. With the presence of the metal patch and metallized vias, the inductance of an equivalent circuit of the antenna is increased, and the capacity is reduced. Finally, the antenna is fabricated on a double-layer FR4 dielectric substrate and measured, and the bandwidth of the compact antenna is up to 26% from 5.1GHz to 6.5GHz in a small size of 0. 5λ ×0. 5λ. The proposed antenna has great potential for compact FTTR products in WLAN applications.
The role of safety climate and exposure to environmental hazards on occupational illness

The Role of Safety Climate and Work Hazards in Experiencing Occupational Illness August 31, 2010 By Matt MacPhee Abstract: This study investigated the relationship between workplace hazards and safety climate and their effect on occupational illness using data from the 2007 Trends in Risk Level questionnaire. Study 1 outlines the history of research into the offshore oil industry in the North Sea, the PSA questionnaire, and presents a process for refining the safety climate measure in the PSA questionnaire. Study 2 took the safety climate scale, along with the exposure to environmental hazard scale, to predict occupational illness. I conducted exploratory factor analyses on the safety climate scale and occupational illness scale. I reported a five factor solution that accounted for 53.9% of the variance. The five factors were: Accident prevention, Threats to safety, Competence, Chemical Knowledge, and Training. Hierarchical regression analysis showed support for the hypothesis that both safety climate explains unique variance in occupational illness beyond what is explained by exposure to hazards. This study investigated the relationship between workplace hazards and safety climate and their effect on occupational illness using data from the 2007 Trends in Risk Level questionnaire. Study 1 outlines the history of research into the offshore oil industry in the North Sea, the PSA questionnaire, and presents a process for refining the safety climate measure in the PSA questionnaire. Study 2 took the safety climate scale, along with the exposure to environmental hazard scale, to predict occupational illness. I conducted exploratory factor analyses on the safety climate scale and occupational illness scale. I reported a five factor solution that accounted for 53.9% of the variance. The five factors were: Accident prevention, Threats to safety, Competence, Chemical Knowledge, and Training. Hierarchical regression analysis showed support for the hypothesis that both safety climate explains unique variance in occupational illness beyond what is explained by exposure to hazards.
Wideband Dual-Polarized Base Station Antenna with Triangular Parasitic Elements

A novel wideband ±45°dual-polarized crossed dipole antenna with triangular parasitic patches is proposed for base station and wireless communication systems. A new resonance is generated by loading the triangular patches between the crossed dipoles, which greatly widens the impedance bandwidth. The proposed antenna achieves a -10 dB fractional bandwidth of 75.9% (1.57-3.49 GHz). The measured inter-port isolation is better than 33.9 dB and the peak gain is 7.4 dBi over the 1.7-2.7 GHz LTE Band. The proposed antenna is well-situated for base station applications.
Optimization of Heat Sink Design by Simulated Annealing Method

This work applies the Simulated Annealing method for multiobjective optimization of aluminum planar heat sink design. Two objective functions are considered independently and combined, first is temperature minimization at the base plate and second is the heat sink volume reduction. A Finite Element Method (FEM) is used to model the thermal field of the design variations under consideration. The optimization FEM model is built using FEMM software, applied via Octave-FEMM interface to Simulated Annealing toolbox in MatLab. Results are showing new capabilities for optimal heat sinks designs with reduced weight and improved heath conduction.
A Futuristic Approach to Synaptic Fusion of INN and CNN Architectures for Tissue Classification

Radiology refers to a branch of medical specialization that focuses on image processing for diagnostics. It has seen immense advances in predictive methodologies in the past few decades, with its predictive capacities being mostly accounted for by machine learning and deep learning approaches. However, classical machine learning has shown comparatively poor performance with regard to non-linear, irrelevant, and highly correlated features in data, and has also shown high reliance on proper presentation of data. This paper aims to improve that notion and aid radiological diagnostics using advanced deep learning and image processing techniques, which will eventually result in improved diagnostic accuracy and productivity of radiologists. This experiment efficiently fuses hybrid neural networks by using CNN (Convolutional Neural Network Architecture) and INN (Involutional neural networks) architectures, which was then compared with other pre-trained models, trained and tested on multiple color models such as RGB (Red, Green, Blue), HSV (Hue, Saturation, Value), and YCbCr (Luminance, Chrominance), to get optimal efficiency. Our proposed Convolutional Neural Network architectures, which include two fused hybrid models of INN and CNN, when trained with carefully handcrafted features, outperform other pre-trained models in terms of micro accuracy.
Utilizing Latent Dirichlet Allocation for Analyzing Topics in Undergraduate Theses

Topic modeling is an important and interesting research area that can assist in discovering patterns and underlying themes in large datasets. This research aims to identify commonly used topics in previous undergraduate thesis research through text mining using Latent Dirichlet Allocation (LDA) as the topic modeling method. The study utilizes abstracts as the primary source to identify the content of the research documents accurately and quickly. We collected a dataset of 666 abstract theses and utilized LDA to identify the dominant themes and topics within the corpus. Our results indicate that the LDA model achieved a coherence score of up to 0.448, indicating a reasonable level of coherence in the identified topics. We identified six main topics within the dataset, including system analysis and design, data mining, computer networks, decision support systems, software testing, and computer security. The result of this study is expected to serve as a useful reference for students and thesis supervisors in selecting future thesis topics and identifying novel and underexplored topics.
The Impact of the Starbucks Mobile Application Loyalty Program on Customer Loyalty

This study was inspired by the successful business methods of Starbucks, the world's largest coffee retailer. Starbucks' revenue-boosting approach is the sale of membership cards. Instead of purchasing purchases, people are fueling their Starbucks accounts. Starbucks is not a bank, Starbucks gives no interest, but the customers enjoy depositing their money in Starbucks cards. Lately, Starbucks has integrated its membership card with the mobile application, making it easier for customers to do transactions. SEM analysis tools are used in this quantitative research strategy. Data was gathered online from 101 Starbucks Card holders in the Jabodetabek area in Indonesia. Data was collected using the Purposive Sampling approach in January 2023. The statistical method used in this study was Structural Equivalence Modeling (SEM) with Smart PLS 4.0. This study aims to identify the elements that influence Starbucks consumers' loyalty. The sampling method employed was Purposive sampling. There are eight variables and nine hypotheses in this research. This study found that two of the nine hypotheses, Privacy and Security Concerns to Utilitarian Value and Customer Satisfaction to Continued Intention, had no effect.
Simultaneous Prediction of Phase and Amplitude Spectrum Based on Convolutional Neural Networks for Reflective Metasurface

The traditional optimization design of metasurface devices relies on experience and trial and error. This method is difficult to apply to complex systems and meet the needs of rapid design, which restricts the development of this research field to a certain extent. In this work, a model based on neural networks is presented for predicting the phase and amplitude spectrum of reflective metasurface. The experimental results show that the response speed of the neural network in predicting the phase and amplitude data of the reflective metasurface reach the millisecond level, and the prediction results are consistent well with the full wave simulation results. This method simplifies the design process of metasurface and has potential application value. It may provide effective auxiliary means for the design and optimization of reflective metasurface.
Printed Ridge Waveguide-Fed Dual-Circularly Polarized Shared-Aperture Array for K- and Ka-Band Satellite Communications

In this paper, a dual-band dual-circularly polarized (CP) shared-aperture array is proposed. The array consists of two 2×2 substrate integrated waveguide (SIW) slot subarrays which can achieve the right-hand CP (RHCP) radiation around 20 GHz and the left-hand CP (LHCP) one around 30 GHz, respectively. Two kinds of sequentially rotated feeding networks using the printed ridge gap waveguide (PRGW) are designed to further improve the axial ratio (AR) bandwidths in both bands. The designed array is simulated and analyzed. It features that the array possesses the -10-dB impedance bandwidths of 11.7% and 11.3%, and 3-dB AR bandwidths of 8.8% and 8.3% in the lower and upper bands, respectively. Besides, the isolation between dual ports is higher than 25 dB.
An Informer Based Method for Stock Intraday Price Prediction

As a component of the capital market, the stock market plays a very important role in economic development, with functions of financing funds and optimizing resource allocation. How to predict the stock price more accurately is the key concern of researchers and investors. To solve the above problem, this paper proposes an intraday price prediction method for the candidate stock based on Informer. Informer based on attention mechanism can efficiently capture precise long-range dependencies between input and output to increase the prediction capacity. In this research, historical K-line data of traditional securities in China’s financial market were collected. Based on this data set, the intraday stock price prediction performance of Informer and cyclic neural network LSTM was compared. The results show that the prediction performance of Informer is obviously higher than LSTM, and it has a good application prospect for the nonlinear intraday stock price prediction.
Gabor-DCTnet: a Simple and Efficiency Method for Image Classification

To address the problem of image classification in which the intra-class variation and inter-class similarity are large due to external interference, thus increasing the difficulty of image classification, a lightweight convolutional network Gabor-DCTnet based on Gabor and DCT filters is proposed in this paper. Gabor-DCTnet cascades predefined filter sets FBGabor-DCT, nonlinear and average pooling operations to make intra-class deformation and inter-class similarity more stable. First, a complex filter FBGabor-DCT consisting of Gabor filters and DCT bases is constructed in the filtering layer to extract rich image features; second, the nonlinear layer performs binarized hash coding and chunked histogram operations on the filter coefficients to obtain robustness in illumination, rotation, and occlusion; finally, the chunked histogram is averaged pooling and wPCA dimensionality reduction in the pooling layer and input to the nearest neighbor classifier based on cosine distance. The experimental results show that the proposed network can effectively classify images on FERET_I, FERET_II, AR and KTH_TIPS, CUReT datasets, and the classification results are significantly better than those of lightweight networks such as PCANet, DCTNet and M-FFC.
Evaluation and Application of Cloud Computing Data Processing Technology for Urban Digital Modeling

Urban digital modeling is the process of digitizing urban three-dimensional space and physical scenes based on computer technology. As a high-performance computing and storage technology, cloud computing has been widely used in the data processing of urban digital modeling. The spatial data of cities involves a wide range and is complex and diverse, requiring the processing of a large amount of information. Traditional data processing methods are no longer sufficient. Cloud computing technology can provide high-performance computing and distributed storage, making large-scale data processing feasible. This paper discusses the combination of urban digital modeling technology and cloud computing data processing technology, and takes Wudaokou area in Beijing as the case study object. The experimental results show that cloud computing platform and algorithm based on digital modeling technology have advantages in urban digital modeling data processing. The research and application of cloud computing data processing technology in urban digital modeling have improved efficiency and accuracy, provided comprehensive and accurate data support for urban planning, management, and development, and promoted the construction and development of smart cities. By analyzing and simulating data, providing scientific basis for urban planning and decision-making can bring enormous potential and opportunities to smart cities.
Semantic Literature Review on Non-Fungible Token: Expansion Area of Usage & Trends

Blockchain, Metaverse & NFT are technologies that were booming during the Pandemic. As a derivative product of blockchain, the Non-Fungible Token (NFT) is one of the technologies that has attracted the most interest from investors and the public because of its potential benefits and has always been closely associated with the Metaverse as a main purposes research of the NFTs. This paper is to explore the possibilities and provide an overview of the current use of NFT technology so that it can provide insight for further research & gap with the big picture that it is trying to present in this paper. However, after exploring further related to NFT in this research, it was found that the problem that most people are trying to solve through NFT is related to the topic of Decentralization & Web3 not as a Metaverse main backbone.