Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured Multi-layer Perceptron Model for fMRI Data

In recent years, the number of functional Magnetic Resonance Imaging (fMRI) datasets sharply increased. As a result, a larger set of methods in artificial intelligence domain, that require a large sample size, have become applicable on fMRI data. This has an impact on many problems in neuroscience, such as brain decoding and biomarker discovery, where we learn to decode the condition of a subject from fMRI recordings.The prominent approaches in data representation of fMRI data for brain decoding are based on surface projection and brain template (graph) representation, that reduce the number of parameters under certain assumptions. In these methods, the lost three dimensional voxel information is not recoverable. Furthermore, these representations become a limiting factor on the brain decoding models.In this work, we propose a multi-layer perceptron model, that jointly incorporates both the 3D brain voxels and the geometric structure of brain regions on the fMRI data. The high number of spatial units in an fMRI sample, compared to the number of samples and the change of fMRI image patterns between subjects are the main challenges in the design of the proposed model.The proposed model is evaluated on various task-fMRI datasets. We observed that the proposed model has on-par performance and significantly lower convergence time, compared to the recent model that requires pretraining on a large source dataset.
Classification of Fungi Images Using Different Convolutional Neural Networks

Fungi offer vital solutions to humanity through roles in medicine, agriculture, and ecological balance while presenting potential threats. They have yielded antibiotics, food fermentation, and nutrient recycling however, fungal infections, crop diseases, and spoilage highlight their dark side. Therefore, it is important to identify fungi to harness their potential benefits and mitigate threats. Offering quick and accurate identification through image classification improves the aforementioned features. Therefore, this study classified images of five types of fungi using convolutional neural networks (CNN). Initially, dataset distribution was observed, and it was identified that there was a class imbalance in the dataset. To address this issue, data augmentation technique was used. Several preprocessing techniques were also applied to understand the model training behavior with their application. Then the images were rescaled into six different resolution combinations such as original images, low-resolution images, high-resolution images, a mix of original and low-resolution images, a mix of original and high-resolution images, and a mix of low and high-resolution images. Then these data were trained using 13 pre-trained CNN models such as Xception, VGG16, VGG19, InceptionResNetV2, ResNet152, EfficientNetB6, EfficientNetB7, ConvNeXtTiny, ConvNeXtSmall, ConvNeXtBase, ConvNeXtLarge, ConvNeXtXLarge, BigTransfer (BiT). To evaluate these models, accuracy, macro average precision, macro average recall, macro average f1score, and loss learning curve assessment were used. According to the results, the BiT model preprocessed with normalization, which used a mix of original and high-resolution images, performed the best, producing a model accuracy of 87.32% with optimal precision, recall, and f1-score. The loss learning curve of the BiT model also depicted a low overfitting aspect proving the model's optimal behavior. Therefore, it was concluded that the BiT model with the mix of original and high-resolution data can be used to detect fungi efficiently.
A Hall-Based Modified Finite Position Set-Phase Locked Loop for Dual Three-Phase PMSM

This paper presents a hall-based modified finite position set-phase locked loop (FPS-PLL) algorithm for dual three-phase PMSM. The novelty of the proposed algorithm is the utilization of the PM flux observer and hall sensors on the motor, which will increase the calculation accuracy and reduce the computational burden simultaneously. Compared with conventional sensorless algorithms with PLL, the proposed algorithm can eliminate the usage of the PI controller. Simulations have been carried out to demonstrate the feasibility and accuracy of the hall-based modified FPS-PLL algorithm.
Cross-ViT: Cross-attention Vision Transformer for Image Duplicate Detection

Duplicate detection in image databases has immense significance across diverse domains. Its utility transcends specific applications, adapting seamlessly to a range of use cases, either as a standalone process or an integrated component within broader workflows. This study explores cutting-edge vision transformer architecture to revolutionize feature extraction in the context of duplicate image identification. Our proposed framework combines the conventional transformer architecture with a ground-breaking cross-attention layer developed specifically for this study. This unique cross-attention transformer processes pairs of images as input, enabling intricate cross-attention operations that delve into the interconnections and relationships between the distinct features in the two images. Through meticulous iterations of Cross-ViT, we assess the ranking capabilities of each version, highlighting the vital role played by the integrated cross-attention layer between transformer blocks. Our research culminates in recommending a final optimal model that capitalizes on the synergies between higher-dimensional hidden embeddings and mid-size ViT variations, thereby optimizing image pair ranking. In conclusion, this study unveils the immense potential of the vision transformer and its novel cross-attention layer in the domain of duplicate image detection. The performance of the proposed framework was assessed through a comprehensive comparative evaluation against baseline CNN models using various benchmark datasets. This evaluation further underscores the transformative power of our approach. Notably, our innovation in this study lies not in the introduction of new feature extraction methods but in the introduction of a novel cross-attention layer between transformer blocks grounded in the scaled dot-product attention mechanism.
Design of 340-GHz On-Chip Power Combiners for InP HBT PAs

This paper presents the design of 340-GHz on-chip power combiners for InP heterojunction bipolar transistor (HBT) power amplifiers (PAs). A two-way Wilkinson combiner is firstly designed, and the measured insertion loss (IL) of back-to-back structure is 2.1 dB at 340 GHz. Then, by cascading two-way T- junction combiners or Wilkinson combiners, three kinds of four- way power combiners are proposed. At 340 GHz, the measured ILs of back-to-back structures of four-way T-junction combiner, four-way Wilkinson combiner and four-way hybrid structure combiner are 1.6 dB, 3.9 dB and 2.3 dB, respectively. It is the first time to demonstrate on-chip power combiners for InP HBT PAs at 340 GHz.
A Miniaturized Tile-Type T/R Module with Four Channels in Ka Band

A miniaturized tile-type T/R module with four channels in Ka band is put forward in this paper. The T/R module consists of 5-bit attenuator, 5-bit phase shifter, T/R amplifiers and control circuit with a size of 8.5mm × 8.5mm, using an amplitude/phase multi-function chip and four T/R multi-function chips. In the receiving mode, the 32-38GHz results in a gain of 25 dB. In the transmitting mode, the output power is 27 dBm for the 6 GHz bandwidth. The measured results show that the tile-type T/R module in Ka band has properties of wide band, miniaturized and high integrated. It has potential for low profile and miniaturized phased control system.
Performance Improvement of Proxy Server Cache Management Using Web Usage Mining

In these modern industries, all sectors are transitioning from manual to web-oriented applications. Thus, the number of Internet users are increasing drastically. Therefore, there is a substantial traffic, which increases the demand on the server and server response latency to obtain web objects. The proxy server caching mechanism is one of the approaches to enhance the performance of accessing web objects via the Internet. Since the cache is typically limited in size, a replacement strategy is required to decide which cached web object should be eliminated to allow spaces for fresh web items. Proxy servers make use of various cache replacement strategies such as Least Recency Used (LRU), Least Frequently Used (LFU), and SIZE. The web objects in the proxy cache are influenced by variables like recency, frequency, fetching time, and size. The traditional caching policies decide only one factor at a time, unpopular web objects are wasted in the cache memory (cache pollution) and the performance of the proxy cache decreases. To increase its performance, we propose using the proxy server log file to identify individual users and their sessions as well as categorize the web objects into three groups: high priority, average priority, and low priority web objects. The prepared log file is used to train the classifiers. Future requests are classified as high, average, or low objects using the classifiers, and it is then chosen whether to store them in the proxy cache or not. The objective of this research is to enhance the proxy caching mechanism by implementing the techniques mentioned above. We compared the performance of the suggested approach with traditional caching policies using a trace-driven simulation method. Two performance metrics, Hit Ratio (HR), and Byte Bit Ratio (BHR), were used for our investigation. Our experimental findings demonstrate that the suggested approach outperforms traditional caching policies.
Design of 1.5-6 GHz High Efficiency 50W Power Amplifier Design for sub 6-GHz 5G Systems

This paper presents the design of a Gallium Nitride (GaN) transistor based power amplifier (PA) with 50W (47 dBm) output power operating in the 1.5-6 GHz frequency range for 5G base stations. In this study, to avoid a gain-bandwidth trade-off by using a single 50W transistor, two 25W transistors are used and combined at the output. A wideband (1.5-6 GHz) 2-way Wilkinson power divider/combiner is designed to drive the 25W transistors by dividing the input power in half and combining the 25W power at the output. Cree CGHV1F025S model GaN transistor and Rogers RT5870 model substrate are used in the design. The performance of the designed 25W PA is evaluated by simulations. According to the simulation results, the 25W PA offers 9.5 dB power gain, 43.6 dBm output power and power added efficiency (PAE) between 42% and 55% in the 1.5-6 GHz band. By combining the output of two 25W PAs with the designed 2-way WPD, an output power of 46.3-47.1 dBm is obtained across the band.
Hydrologic flowpaths during snowmelt in forested headwater catchments under differing winter climatic and soil frost regimes

Changes in hydrologic flowpaths have important impacts on the timing, magnitude and hydrochemistry of run‐off during snowmelt in forested catchments, but how flowpaths are affected by variation in winter climate and the irregular presence of soil frost remains poorly understood. The depth and extent of soil frost may be expected to increase as snowpack decreases or develops later because of climate change. In this study, we used end‐member mixing analysis to determine daily contributions of snow, forest floor soil water and groundwater to stream run‐off during snowmelt under different soil frost regimes resulting from interannual and elevational variation at the Hubbard Brook Experimental Forest in New Hampshire, USA. We observed greater routing of run‐off through forest floor flowpaths during early snowmelt in 2011, when the snowpack was deep and soil frost was minimal, compared with the early snowmelt in 2012 under conditions of deep and extensive soil frost. The results indicate that widespread soil frost that penetrated the depth of the forest floor decreased the flow signal through the shallowest subsurface flowpaths, but did not reduce overall infiltration of melt waters, as the contribution from the snow‐precipitation end‐member was similar under both conditions. These results are consistent with development of granular soil frost which permits vertical infiltration of melt waters, but either reduces lateral flow in the forest floor or prevents the solute exchange that would produce the typical chemical signature of shallow subsurface flowpaths in streamwater. Copyright © 2016 John Wiley & Sons, Ltd.
Signal Strength, Delay, and Throughput Performance Comparison of 4G, 5G, and 6G Networks in VANET

Traffic bottlenecks and more autos plague urbanites. Ad-hoc vehicle networks boost network traffic. VANET lets cars and road infrastructure communicate. Driving safety and economy are improved via 6G traffic simulation. This study compared 4G, 5G, and 6G VANET performance. At PKU Muhammadiyah Hospital Yogyakarta, this quantitative study compared VANET communication performance using 6G, 4G, and 5G technology. Latency, throughput, and dB signal strength measure network performance. Average signal strength of 11,531 dB on 4G, 19,334 on 5G, and 19,490 on 6G. Delay studies average 159,607 ms on 4G, 158,607 on 5G, and 157,607 on 6G. 4G has an average throughput of 427,421 Kbps, 5G 430,421 Kbps, and 6G 432,421 Kbps. Vehicle networks use V2I and V2V communication, and 6G provides improved signal strength, latency, and throughput than 5G and 4G.
Throughput Optimization Control Strategy for Mobile Wireless Sensor Networks Under Rician Fading Channels

This paper introduces a distributed control strategy aimed to optimizing the end-to-end throughput in mobile wireless sensor networks operating in the presence of Rician Fading. On an end-to-end route, sensor nodes self-classify themselves as either critical or non-critical nodes. The critical nodes execute throughput optimization of the link connecting with the preceding node in the route by re-configuring its position using Particle Swarm Optimization (PSO) Algorithm. We propose a sequential optimization strategy that enables re-configuration activation in a sequential manner from the source to the destination, overcoming the limitation of the throughput optimization method relying on a coordinator. Simulation experiments affirm that the proposed strategy significantly enhances end-to-end throughput and eliminates repeated reconfiguration processes.
Why People Want To Use E-money Card based On RFID Technology ?

Radio Frequency Identification (RFID) is a technology that is used widely for mobile payment, the security is the main issue of the usage of this technology. The aim of this study is to identify the key factors influencing interest in utilization electronic money card based on RFID Technology although the security is the main issue. This research investigates factors of perceived Security, perceived benefits, and consumer trust intention to use e-money. This research uses a quantitative method using TAM as a reference model. In this research, the Partial Least Squares Structural Equation Modeling (PLS-SEM) was utilized as a data validation system. To examine our hypotheses, the Bootstrapping method was applied using SmartPLS. The results show that the perceived security and perceived benefits have a significant effect on user trust intention. The perception of security is the most important factor that encourages consumers to use electronic money in Indonesia.
Leveraging Artifact Reputation Analysis and Contextual Sentiment Analysis for Advanced Detection of Vishing and Smishing Attacks

The rise of advanced mobile technology has brought about the widespread presence of mobile devices in our society. These portable and versatile gadgets have become essential items for individuals due to their convenience and capabilities. As technology continues to play a pivotal role in modern life, an ever-growing number of people rely on mobile devices for almost all life activities including crucial financial activities and business routines. However, the increasing popularity of mobile devices has also exposed users to a heightened risk of falling victim to fraudulent schemes. Perpetrators have been exploiting mobile users by pretending to present authentic and legitimate requests and opportunities, leading to the divulgence of personal and sensitive information. These deceitful activities have seen a significant increase, affecting individuals of various ages, educational backgrounds, and levels of technological literacy. Additionally, malicious actors employ advanced methods to conceal their identities, making it challenging to prevent and counter these attacks. Two prevalent yet under-addressed issues in this context are vishing and smishing. This research study introduces a system designed to detect vishing and smishing attempts more accurately. The system analyzes the reputation of suspicious artifacts in messages and call conversations using third party threat intelligence services. Further, it employs natural language processing and machine learning techniques to examine the content of voice calls and SMS messages. It identifies suspicious elements such as keywords and phrases commonly used in phishing attacks, sensitive information as well as the context of the content.
Improving Gaussian Sum Filter to Implement In-motion Yaw Alignment for Quadrotor Attitude Control

This paper presents a new type of integration between the Global Positioning System and Inertial Measurement Unit (GPS/IMU) to provide a higher in-motion yaw alignment accuracy under large initial heading errors when magnetometer data are not reliable. In order to quickly determine the yaw angle without relying on assumptions about the dynamics of the quadrotor, velocities measured from GPS are fused with accelerations and angular velocities measured from the IMU. In contrast to the conventional Gaussian sum filter (GSF), in this paper the forecast weights of the Gaussian mixture model are updated in the time update step of basic estimators, including complementary filters (CF) and extended Kalman filters (EKF). This reduces the number of Gaussian models used in the GSF and speeds up computation on embedded system hardware.
A Compact 24-30GHz Doherty Power Amplifier with a Flexible T-Type L-C-L Series Combiner and New Power Sensing Method for 5G Communications

This paper presents a series Doherty power amplifier (PA) fabricated in 40nm CMOS for fifth-generation (5G) millimeter-wave (mm-Wave) communications. A novel series Doherty combiner is synthesized by lumped T-type L-C-L elements, which models the interconnection more accurately and provides better agreement between behavioral and electromagnetic (EM) simulations. A new power sensing method is proposed by extracting the interstage voltage of the typical adaptive bias circuit (ABC). This PA is implemented in a compact layout with a core size of 0.22mm2. The measured 3-dB small-signal bandwidth is 23.0-31.1GHz. The PA achieves a peak $\mathbf{P}_{\text{sat}}/\mathbf{P}_{1\mathrm{d}\mathrm{B}}$ of 18.0/17.4dBm, while the peak $\text{PAE}_{1\mathrm{d}\mathrm{w}}$ and $\mathbf{PAE}_{\text{sat}-6\mathrm{d}\mathrm{B}\}}$ are 29.4% and 24.5%, respectively. The PA has been tested with a 64-QAM 100MSym/s modulated signal at 27GHz and achieves $\mathbf{P}_{\text{avg}}, \text{PAE}_{\text{avg}}$ of 9.1dBm/16.9% while $\text{EVM}_{\text{RMS}}$ and ACPR is −24.4/−25.7dBc. The 16-QAM 500MSym/s signal has also been tested at 27GHz and the PA obtains Pavg, $\text{PAE}_{\mathrm{a}\mathrm{v}\mathrm{g}}$ of 7.1dBm/13.0% while $\text{EVM}_{\mathrm{R}\mathrm{M}}\mathrm{s}/\text{ACPR}$ is −20.9dB/−21.7dBc.
Visualizing Attitudinal Dynamics of Moving Objects via Android: Capturing Pitch, Roll, and Yaw Parameters

In recent times, smartphones can be said to have become a necessity for users. From children to old age, everyone knows what a Smartphone is. The smartphone has various uses according to its users and is equipped with hardware with various sensors. The sensors that each smartphone owns also vary depending on the original factory. Some of the sensors owned by smartphones have beneficial uses; for example, magnetometer sensors do use to determine the wind direction. Then there is the Accelerometer and Gyroscope sensor, which will form an orientation sensor to discover the changes the smartphone feels at an angle, which developed in an application. This study emphasizes the value of the Pitch, Roll, Azimuth, and Angle sensors, which will form a graph according to the behavior of the smartphone. The overall experimental outcomes were obtained by conducting a comprehensive comparison against applications available on the Play Store, ensuring that the margin of error remained below 5 degrees. The orientation sensor is intended to get the value of the attitude change by the smartphone, whose value will then form a graph in real-time. The values preserved results will storing in the Cloud Firebase.
Personal Loan Default Prediction and Impact Analysis of Debt-to-Income Ratio

Loan defaults affect the financial sector, particularly impacting banks and lending institutions, resulting in a rise of non-performing assets and financial strain. To counteract this trend, traditional credit assessments use methods like credit scores and exploitation of socio-demographic composition of the customers. However, customers may possess numerous debt obligations that credit bureaus uncover, which can help to measure their repayment ability. This study proposed a comparative methodology that leverages five machine learning algorithms to predict personal loan defaults using debt-to-income ratio apart from the credit scoring models that prevail at banks. It analyzed the impact of debt payments on loan defaults and applied ensemble clustering to categorize customers’ risk levels based on their debt-to-income ratio. Experimental results indicated that ensemble clustering has enhanced the prediction power compared to conventional classification models to predict loan defaults.
Nonlinear Model Predictive Control for Ballbot systems: A benchmark with Hierarchical Sliding Mode and Linear Quadratic controls

This study presents the Nonlinear Model Predictive Control (NMPC) method for ballbot systems. Due to the complex nature of the underactuated system, ballbot requires a controller capable of ensuring both goals: moving to the desired position and maintaining the balance of the body throughout the moving process. NMPC is an optimal control method based on a predictive model to calculate the states of the ballbot after a certain period of time in the future. Moreover, NMPC can optimize the position, tilt angle and control signal through the cost function and handle special constraints of the system. Simulation results show that NMPC is more effective than some previous techniques such as LQR and HSMC in terms of safety constraints of tilt angle and input-output signals.