Miracle-dz/UniFAR-FTUs huggingface dataset:
Zero Trust Black Network Access for Mobile Broadband Mission-Critical Services

Reliable communications are an important part of public missions. Previously, public safety networks used dedicated networks based on narrowband technology. However, there is current interest in leveraging mobile-based BroadBand(BB) technologies. Therefore, in this paper, we discuss technologies named ZTB(Zero Trust Blacknet) that can safely access Mission- Critical(MC) services in broadband mobile-based public safety networks. A mixture of rapidly deployable tactical bubbles and commercial access networks, zero trust zones require rigorous verification before access to MC server zones. First, we made a kind of tactical bubble with Open5GS and srsRAN. Next, mobile communication with commercial mobile devices takes place in this bubble. We also develop the ZTB Manager, Gateway and Agent functions to provide secure access to the MC server based on the Zero Trust Architecture(ZTA) and Single Packet Authorization(SPA) mechanism. ZTB will help increase the reliability of communications over mobile broadband public safety networks.
The Influence of Readiness to Use Technology and Creativity on Task Performance: Perspectives from the Fashion Creative Industry in Bandung City

It cannot be denied that technology is one of the factors driving business competition. Thus, technological preparation is one of the determining factors in winning business competition. In the creative industry sector, fashion requires a high level of creativity. So, it is necessary to prepare employees to use technology that can increase creativity, which has implications for employee work; this study focuses on predicting the construct of technological readiness that impacts employee creativity and its implications for completing employee assignments in the fashion industry sector in Bandung. The 243 respondents involved with the structural equation-PLS model prediction approach showed different results where the overall construct of technological readiness can increase employee creativity and have implications for performance. The research results indicate that efforts are needed to select innovative, easy, comfortable, and safe technology. So, it can speed up work productivity. The results of this research cover the gap in previous research, showing that yet to be research has examined the effect of technological readiness and creativity on increasing employee work in the fashion creative industry.
One-Class Support Vector Machine for WiFi-based Device-free Indoor Presence Detection

—The utilization of existing radio signals such as 802.11 (WiFi) for device-free detection of human presence and movement indoors has garnered signiﬁcant interest among researchers in academia and industry. Improving the e ﬃ ciency of buildings, particularly in terms of heating and energy costs, relies on accurately detecting room occupancy. Our approach uses channel state information (CSI) obtained from commodity 802.11ac hardware as input to machine learning based on One Class Support Vector Machine (OC-SVM). Unlike other methods that necessitate extensive learning in environments with and without human presence, our approach treats human presence as a novelty. This simpliﬁes the training process, as we only need to learn from environments without human presence, speciﬁcally empty rooms. Furthermore, since we focus solely on analyzing the magnitude information of the CSI data, there is no requirement for intricate sanitization of the phase information. Experimental results using standard WiFi hardware demonstrate exceptional performance, with accuracy, sensitivity, and speciﬁcity exceeding 97% in most cases. Furthermore, our proposed approach is practical, as it incurs minimal overhead in terms of radio resource usage. Simply capturing CSI data with a sampling rate of 5Hz on only a few OFDM subcarriers from a 5MHz channel is su ﬃ cient.
A 30GHz Bidirectional PA/LNA with Transformer-Based Switchable RC Matching Network

This article demonstrates a 30GHz bidirectional PA/LNA aiming for area-efficient millimeter-wave (mmWave) front-ends for multiple-in-multiple-out (MIMO) phased array applications. To address the problem of unwanted turn-on of LNA devices in PA mode, hybrid P/NMOS structure is employed. A transformer-based switchable RC matching network is proposed to improve both PA and LNA performance while maintaining wideband matching. To improve gain and linearity of PA/LNA, high-precision cross-coupled capacitance neutralization is realized and tail switch device is used in place of tail bias device. A single-stage hybrid N/PMOS bidirectional PA/LNA is designed and simulated in a 40-nm bulk CMOS technology. Simulation results show that the proposed bidirectional PA/LNA achieves 10GHz bandwidth and 13dB gain with a single amplifier stage. The PA achieves 34.7% maximum power-added efficiency (PAE), 12.5dBm output power at output 1-dB compression point (OP1dB) while the LNA achieves 3.8dB minimum noise figure (NF) 6.9dBm input 3rd-order intercept point.
A chopper-type mixed gait controller based on ergodic cellular automaton central pattern generator

Here, we designed an extended ergodic cellular automaton model of a central pattern generator. The designed model is able to synthesize the phenomena of synchronization, which is able to realize standard gaits of a quadruped robot. We show that the mixing of the coupling matrices on the basis of the chopper-like method has the following properties, the designed model is able to achieve mixed gaits for the quadruped robot based on the matrix ratios in the chopper.
Vision-assisted Beam Prediction for UAV-enabled Millimeter-Wave Communications using SE-ResNet50

The high overhead associated with the beam training process poses a significant challenge for highly mobile applications such as UAV communication. To mitigate this issue, this study proposes Squeeze-and-Excitation (SE) network that leverage visual information for accurate beam prediction in mmWave UAV communication. The SE network can selectively emphasize informative features through channel-wise feature recalibration, which enables the network to adapt to changing conditions, and optimize its predictions for different scenarios.
Subject Classification and Difficulty Ranking of Math Problems

Education is a stage that everyone is bound to go through. With the development of technology, the quality of education has also advanced a lot. But the improvement in quality does not improve the learning performance of students equally. E-learning tools such as mobile language learning apps and mock test sites often only provide a uniform set of learning material and study plan. As a result, one popular research trend is to identify student’ learning progress with the aim of customizing instruction to better meet their needs. In this paper, deep neural network models are used to classify the subjects (e.g. algebra, geometry) of math problems and rank their difficulty levels. To address the problem of classification, we extend deep learning models based on LSTM and BERT. The experiment results show that the LSTM model initially has only 59% accuracy, but after exploiting structure of math expressions through feature engineering, the LSTM model can accomplish up to 75% accuracy, close to the accuracy (79%) of the more sophisticated BERT model. Moreover, it was found that these classification models can learn feature representation that is useful in the task of ranking difficulty level of math problems. This is verified through an experiment comparing transfer learning with direct learning. This work shows that the deep learning models can handle the subject classification and difficulty level ranking of math problems. This has the potential to generate a more refined assessment of the student’s domain knowledge, and help to recommend more focused review exercises to teachers and students.
The application of style transfer algorithms in the graphic design of Yunnan tea carvings

This study aims to promote the development of ecological packaging for Yunnan tea carvings and enhance the economic value of the local cultural industry. It involves an in-depth analysis of the graphic elements found in Yunnan tea carving craftsmanship. By integrating these elements with advanced deep learning techniques, the study enables the rapid generation of innovative texture patterns specifically designed for Yunnan tea carving. The application of style transfer algorithms to these graphics facilitates pattern self-renewal and improves the efficiency of pattern design in Yunnan tea carving. Additionally, this research explores the utilization of artificial intelligence in tea carving products as a means of creating folk ecological packaging. By doing so, it not only elevates the cultural and economic value of Yunnan tea carving products but also opens up new possibilities for integrating traditional cultural art with modern technology.
A 94.3 % Peak Power Efficiency Time-Based Buck Converter Using Pulse-Phase-Shift Modes with An Intrinsic Window for Transient Enhancement

This paper proposes a time-domain controlled buck converter with fast transient response. The dual-mode control consisting of pulse-width modulation (PWM) and pulse phase shift-pulse train (PPS-PT) modes is utilized to suppress the voltage ripples during the steady state and enhance the transitions between the light and heavy load, respectively. The edge-pursuit comparator (EPC) is adopted to detect the voltage difference and decide the operation mode when the load varies. Compared with conventional hysteretic window control, lower power consumption, and better transient response are achieved. Measurement results show that when the load varies from 50 mA to 450 mA, the recovery time is 2.5 μs and the undershoot is 100 mV. The overall peak power efficiency is 94.3 % and a FoM of 1.68 mA*%/μs*mV is accomplished.
Air Quality Index Mapping Using Programmable Single Propeller UAV Towards Internet of Drone Things

Unmanned Aerial Vehicles (UAVs) with the advances in technologies are becoming more common, UAV has become a platform to gather data that humans simply cannot reach. With the mobility of a drone, it can cover a lot of areas in a single take, greatly benefiting the data gathering process. However, conventional model drones like quadcopter configurations are big and costly for mass implementation such as in Internet of Drone Things. To handle these issues, this research proposed the use of a Single propeller drone design, drone with only 1 propeller, that can lower the cost of making the drone unit and ease the data gathering process. The single propeller drone gathers data for the air quality measurement device that uses DHT22 as the humidity and temperature sensor and MQ-135 as the air quality sensor. From the test result, carried out on Sukapura Football Field, Bandung, Indonesia, the single propeller was able to obtain the air quality, sent it to Google Firebase, and displayed it using smartphone. The test showed a slight difference result in the air quality data between different placements of the sensors on the single propeller drone. The test also showed the difference between stationary single propeller drone, which gave 103 ppm, with hovering drone, which gave 124 ppm. Furter test showed there were no differences between temperature and humidity result, with a maximum of 24oC and 99.6% humidity. Besides the differences in the data quality, the test result was near accurate.
Study on Implementation of PWM Dimming in Beam Angle Switched LED Street Light to Achieve Energy Efficient Standard Lighting

To study the effectivity of implementing PWM dimming in conjunction with BAS (Beam Angle Switching) method to a low powered LED street light and the effect of varying PWM (Pulse Width Modulation) duty cycle on the overall Illuminance Uniformity (Uo) of the working plane (here the simulated street under controlled environment) under a low wattage LED street light and to find any co-relation between duty cycle and overall illuminance uniformity and other lighting parameters like CCT, CRI and Illuminance, a decorative experimentation is done in Illumination Engineering Laboratory of Electrical Engineering Department of Jadavpur University. The percentage PWM duty cycle precisely refers to the proportion of time that a digital signal is on over the period of time or an interval. The waveform's frequency is inversely proportional to the period of the signal. Besides, overall Illuminance uniformity is the ratio of minimum illuminance value and average illuminance value found from a particular grid, drawn under the experimental street light. In this study, PWM dimming is successfully implemented with BAS method in a 55-Watt LED Street light. Moreover, study shows that overall Illuminance Uniformity does not change significantly with the change in PWM duty cycle, further indicating that PWM dimming can be easily incorporated with BAS method and it would be beneficial from energy consumption reduction.
An Approach to Translate Synthetic Lidar Point Cloud Data Similar to Real Lidar Data and Evaluate Using Deep Learning Techniques

As autonomous driving technology advances, the need for 3D LiDAR point cloud data has grown, particularly for handling unpredictable daily travel scenarios. However, it typically requires large annotated datasets for training, posing challenges in the 3D semantic segmentation models which is time-consuming and expensive. To address this, synthetic data from simulators is considered, but it lacks realism compared to actual LiDAR data. This study introduces a method that translates synthetic point clouds to closely resemble real data, enabling their use in real-time applications with limited actual LiDAR data. The proposed method combines Point Cloud Translation (PCT) and Domain Adaptation (DA) techniques, along with the state-of-the-art Cylinder3D segmentation network for 19 classes. Along with the above generation, comparative evaluations of various mixing techniques on synthetic and domain-translated data determine the optimal ratios for 3D semantic segmentation when combined with real data. SynLiDAR serves as the synthetic source data, and Semantic Kitti serves as the target data. By combining synthetic and domain-translated data with real data, the proposed model achieves mean Intersection over Union (mIOU) scores of 63.954 and 64.578, surpassing the mIOU of real data alone using the Cylinder3D segmentation network.
DRL-Based Satellite Network Slice Planning and Handover in the Korean Peninsula Scenarios

This paper introduces a novel approach using deep reinforcement learning (DRL) to enhance network slicing planning and handovers in satellite networks. We propose a proactive handover trigger based on remaining service time and employ the deep deterministic policy gradient (DDPG) algorithm to maximize the utility of virtual networks (VNs). Focusing on the Korean Peninsula, we simulate a low earth orbit (LEO) satellite network based on Starlink satellite specifications and demonstrate the superiority of our intelligent network management technique compared to baseline methods, particularly in terms of latency performance and the number of handovers.
Improved Channel Estimation Algorithm Based on Light Communication System

In this paper, we propose the concept of threshold for channel estimation in optical communication systems. , and the system threshold is determined by LS, MMSE, LS-DFT, MMSE-DFT and DCS-SOMP channel estimation algorithms. The MMSE-DFT channel estimation algorithm is combined with the DCS-SOMP channel estimation algorithm to improve the termination condition of the algorithm iteration, and the threshold is used as the decision condition of the algorithm. The simulation results show that the MSE of the improved algorithm is reduced by 3.52dB and 5.13dB respectively compared with the MMSE and LS channel estimation algorithms when it is less than the threshold (SNR=15dB); the MSE of the improved algorithm is 7.51dB and 8.99dB lower than that of the MMSE-DFT algorithm and LS-DFT algorithm respectively when the SNR is greater than the threshold (SNR=20dB). The mean square error improvement of the proposed scheme has been significantly improved.
Farm Financial Performance: Broadacre Cropping Incomes Fall but Incomes for Beef Farms Strengthen

Lower winter rainfall across much of Australia's wheat and winter grain producing regions in 2004 reduced overall crop production and, in combination with lower international grain prices, has sharply reduced the forecast incomes of grain farms in most states in 2004-05. Incomes for broadacre livestock producers are forecast to increase in 2004-05 despite lower wool prices. Higher prices for beef cattle combined with increased cattle turnoff and continuing strong sheep and lamb prices are driving incomes higher for livestock farms. Increases in international dairy prices, higher milk production and lower feed prices are expected to result in incomes for dairy farms also increasing in 2004-05. High demand for agricultural land in the past four years has resulted in significant increases in farm values, boosting farm equity.
Research on Neural Network Digital Pre-distorter Based on Transformer

The design of 5G communication systems requires wider transmission bandwidth and higher modulation levels, which necessitates the linearization of power amplifiers (PAs) to mitigate their nonlinearity. Neural network (NN) models, such as back propagation (BP) and long short term memory (LSTM), have been applied in the field of digital predistortion (DPD) and have become a new solution for radio frequency power amplifier (RF PA) linearization. This paper proposes a Transformer-based NN digital predistorter that utilizes the multi-head self-attention mechanism of the model to simultaneously capture the global temporal relationship and the dependency between signal features, constructing better signal nonlinear characteristics and memory effects for the current signal in the lookahead term. The model improves the prediction performance and parallelism of the model, accelerating the training speed. The experimental results show that the model improves the adjacent channel power ratio (ACPR) index by about 12dB compared to the output signal without DPD processing, and reduces the parameter volume by 57% compared to the LSTM network with similar accuracy. Meanwhile, the model's normalized mean square error (NMSE) index reaches -39dB, which is about 5dB higher than that of the traditional BP NN model.
Growth of Large-Area All-Inorganic Single Crystalline Lead Halide Perovskite for Photosensors

Photosensor which converts the optical signal into electrical signal is an essential element in optoelectronic integrated circuits. Lead halide perovskites have been widely investigated for photosensor applications due to their excellent photoelectronic properties and superior stability. Moreover, single crystalline perovskite films have fewer defects comparing with polycrystalline films, which can further improve the material properties. In this study, vapour phase epitaxy was used to grow large scale single crystalline CsPbBr3 thin films with lateral sizes up to 2.25 cm2, Microstructure characterization of the films reveals that they are of high quality, with smooth surfaces. Furthermore, photo sensors constructed on these single crystalline films exhibits excellent on/off current ratio of 2.4×105. Our work not only presents a facile method for producing large area CsPbBr3 single crystalline films but also provide a platform for developing high-quality halide per-ovskite photo sensors and their large-scale integration.
Dynamic Scheduling Algorithm of Multiple New Energy Complementary Power Load Data

The output of new energy is affected by a variety of factors, such as weather, season, geographical location, etc., which makes the output of new energy has great instability, makes the node load concentrated, resulting in too long task scheduling time. Therefore, it is necessary to consider the complementarity of various new energy sources to design a brand-new dynamic scheduling algorithm of power load data. That is, considering the complementarity of various new energy sources, an effective dynamic scheduling transmission protocol for power load data is selected, and a dynamic scheduling strategy for power load data is designed, thus generating an optimization algorithm for dynamic scheduling of power load data. The experimental results show that the designed dynamic load data scheduling algorithm of various new energy complementary electric power has a short scheduling time, good performance and high efficiency, and has certain application value. It makes a certain contribution to the subsequent optimization of power supply resource allocation, reduces the power cost and improves the economic benefits of electric power enterprises. At the same time, promoting the development of new energy will help promote the application of renewable energy and achieve sustainable development.
A High-Efficiency Rectifier with BPSK Modulation Wave for Simultaneous Wireless Information and Power Transmission

This paper studies the rectifier for binary phase shift keying (BPSK) modulation wave for simultaneous wireless information and power transmission (SWIPT). Firstly, the theoretical analysis of BPSK signal rectification is carried out from the waveform expression. It is concluded analytically that the rectifying efficiency would increase as the phase interval of BPSK signal becomes smaller. Secondly, the rectifier with a double-branch matching network for BPSK signal is designed using ADS software. With the input power of 12.8dBm and the load resistance of 600$\Omega$, the maximum rectifying efficiencies at 5.8GHz are S1.2% and 7S.6% for continuous wave (CW) and BPSK signal, respectively. The simulation results validate the theoretical conclusion.
Brain Tumor Segmentation using Attention-based UNet

Throughout its existence, Brain Tumor Segmentation (BraTS) challenge has always been researched applying cutting-edge methods for segmenting brain tumors in magnetic resonance imaging (MRI) images. Using a combination of different MRI scans before surgery, one primary objective here is to segment gliomas according to their shape, appearance and histology (Task 1). To identify whether this segmentation task is clinically relevant, BraTS 2020 dataset can also be utilized in a comprehensive manner with a combination of machine learning algorithms and radiomic data to predict patient survival (Task 2), as well as differentiate between real recurrence of tumours and hypothetical progression (Task 3). The final task of BraTS 2020 is to analyze the uncertainty associated with tumour segmentation algorithms (Task 4). In this work, we mainly focus on task 1 of the BraTS 2020 dataset, which aims to segment gliomas that are inherently heterogeneous in their appearance, morphology, and histology.
Cache Node Placement for Maximum Traffic Minimization in Content-Centric Networking

Content-centric networking (CCN) is a network architecture based on content. The conventional network architecture uses IP address to route packets based on location. In CCN, which routes packets based on content name, a cache function can be attached to nodes. Getting contents from cache nodes alleviates traffic congestion in the network. In the case that the cache function is not attached to all nodes in the network, it is necessary to decide where cache nodes are placed. Congestion tends to occur around cache nodes when using the conventional method. We formulate a problem of cache node placement as an integer linear programming (ILP) considering the population of each node. The ILP formulation determines the cache node placement to minimize the value of the maximum traffic. This result shows the proposed ILP approach reduces the maximum traffic by 59% on average.