Miracle-dz/UniFAR-FTUs huggingface dataset:
In Vivo Skin-Type Classification Using Millimeter-Wave Near-Field Probe Spectroscopy

This paper presents a methodology for millimeter-wave-based in vivo classification of human skin into the two naturally occurring skin types: thick skin with low water content and thin skin with high water content. For medical diagnostic applications in the microwave range, the distinction of the water content plays a major role. The method presented is based on the resonance characteristics of reflective skin measurements. Therefore, in vivo reflective spectra were measured on 10 individuals. For the measurement acquisition, a 3D printed rectangular waveguide-based near-field probe was designed that is sensitive to the skin water content in the frequency range of 75–110 GHz. In total, 360 reflection spectra were obtained, received from six different areas on the hands and arms of the volunteers. A support vector machine (SVM) model with two classes, thick skin on the palm and thin skin present on the arm and back of the hand, was trained with the measured data. The resulting SVM classifier was evaluated to have an accuracy of more than 95%. The high classification accuracy verifies the skin type classification approach as a simple and appropriate in vivo two-level hydration model for the human skin. On the basis of these results, complex challenges such as burn wound classification can become feasible, since ex vivo burn measurements have been shown to display equivalent resonant characteristics.
Early Risks Depression Prediction of Indonesian Twitter Users Through Indonesian Text Using Transfer Learning and Linguistic Metadata Features Approaches

Depressive disorders are the first rank of mental disorders that cause DALYs (Disability Adjusted Life Years) with a death contributor percentage of around 14.4%. Currently, depression has been detected based on self-reported by sufferers, behavior reported by sufferers’ friends, and mental health examinations. This work was detecting depression based on social media data using machine learning approaches. Machine learning algorithms are regarded as effective nowadays for completing jobs involving natural language processing with vast amounts of tagged data. In contrast to English, the depression (Indonesian text) classification data set still has gaps. To overcome the issue we combined Indonesian and English data with three different experiment scenarios–monolingual, zero-shot, and multilingual–this study employs pre-train language models (Multilingual BERT and XLM-RoBERTa) and Linguistic Metadata Features. In the course of the experiment, 55 relevant individuals and 4,465 Indonesian tweets were employed. Additionally, the performance of the model was enhanced by using 476 people and 51,005 pieces of Reddit data. The experimental validation data showed that the use of linguistic metadata features has no discernible impact on predicting depression’s user. The highest FI score obtained utilizing a zero-shot scenario, a multilingual BERT, and a learning rate of le-5 was 0.62. Additionally, an F1 score of 0. S15 is achieved in an XLM-RoBERTa fine-tuning scenario with a learning rate of le-5. It was successful in surpassing the baseline approach that was again used, with an F1 score of 0.615.
Multi-scale traffic sign detection algorithm based on improved YOLO_V4

Traffic sign detection and recognition is a key technology for intelligent assisted driving. This paper proposes a key technology for traffic sign detection and recognition based on improved YOLO_V4 in complex urban scenes. The technology uses K-means++ clustering to generate an anchor that conforms to traffic signs; designs a CSPDarknet33 backbone network that greatly reduces the number of weight calculations optimizing the structure of the YOLO_V4 neural network; uses Focal Loss and balanced cross entropy function to optimize the loss function of YOLO_V4, and completes the traffic sign detection work. Then this paper designs an improved VGG16 neural network, which is used to train three models that recognize warning signs, prohibition signs and indication signs. Finally, according to the results of traffic sign detection, the traffic sign recognition work is further completed, the mAP is 94.48% and the FPS is 27, its detection performance is significantly higher than other networks.
Learning-based Motion Control of Autonomous Vehicles Considering Varying Adhesion Road Surfaces

Advanced motion control of intelligent vehicles, e.g., trajectory tracking still faces challenges in enhancing stability on varying adhesion roads, particularly for driving at high speed with strong uncertainty in tire-road adhesion. The learning-based Gaussian process (GP) model can be used to mitigate the adverse effect arising from the state noise and model mismatch. However, it is challenging to cope with strong variation under diverse road conditions. This research proposes a multi-model control framework to enhance motion stability by capturing and reducing tracking errors on varying adhesion roads. We build three error models for high, middle and low adhesion road surfaces separately, and propose a switching strategy that can choose an appropriate model for the current road adhesion condition. Simulation results show that the proposed control strategy enhances the stability during trajectory tracking on complex roads with large curves and strong uncertainty, e.g., in case of a sudden change of road adhesion.
A 28 GHz 22FDX® PA with 31.5 % Peak PAE and Output Power of 21 dBm in CW, 18.5 dBm in QPSK, and 12.5 dBm in 64QAM

A 22nm FD-SOI Power Amplifier (PA) for 5G communication at 28 GHz is demonstrated. A dual-stage approach together with stacking techniques and optimized transistor-interconnection layout is used to achieve a measured peak PAE above 31% with an output power of 21.0 dBm. Besides output power and PAE, the PA demonstrated 12.5 dBm at −28 dB EVM with 64-QAM CF -OFDM 5G modulated signal and 18.5 dBm with DFT-s-OFDM QPSK modulated signal at −19 dB ACLR with 100-MHz bandwidth with a linear PAE of 20%. To the best of the authors knowledge, the presented PA shows the highest linear power and efficiency with QPSK while its 64-QAM modulated output power outperforms by 1 dB the state of the art while also reducing the EVM by 1 dB, when compared against 2-stage PA silicon implementations. Finally, an output-power degradation below 0.05 dB after 66 hours of stress at 21 dBm power level has been shown to demonstrate excellent reliability.
Coordinated traction control of intelligent four-wheel-drive vehicles under complex start up situation

With the development of autonomous vehicles technology, information about vehicles and the environment could be obtained more effectively and accurately. Intelligent four-wheel-drive (4WD) vehicles could allocate the drive torque to the front axle and rear axle by the transfer case. The coordinated control of the transfer case, the brake system, and the engine in the intelligent 4WD vehicle could improve the performance of the vehicle. The information could be used to improve the control performance of vehicle chassis in intelligent 4WD vehicles. In this paper, a coordinate controller has been developed to start up the vehicle under complex road surfaces, a model is built for simulation, and the results show that the controller could handle the complex situation.
Using a Distance between Fuzzy Number-Valued Fuzzy Measures to Determine Attribute Importance: An Outcome-Oriented Perspective

The purpose of this paper is to introduce a new method for measuring attribute salience in decision analysis. Fuzzy measures have been widely used to determine the degrees of subjective importance of evaluation items. We consider the value of fuzzy measures as a linguistic value and then convert linguistic terms to fuzzy numbers. The grades of attribute importance are correspondingly expressed by fuzzy number-valued fuzzy measures. However, the leniency error may exist when most attributes are assigned unduly high ratings. To reduce positive leniency in fuzzy measure ratings, we develop an importance-assessing method by comparison of fuzzy number-valued fuzzy measures using a fuzzy distance measure. An outcome-oriented approach is adopted to validate the proposed method. Several multiattribute evaluation cases in consumer decision-making reality are addressed to examine the feasibility and applicability of the current method.
Dynamical Analysis of the Structure of Neutron Star Critical Collapses

Jin et al reported that axisymmetric simulations of NS-like objects with polytropic EOS undergo critical gravitational collapse. As the critical collapse observed via fine-tuning of the adiabatic index $\Gamma$, they conjecture that critical phenomena may occur in realistic astrophysical scenarios. To clarify the implications this numerical observation has on realistic astrophysical scenarios, here, we perform dynamical analysis on the structure of the critical collapse observed in the former work. We report the time scales and oscillation frequencies exhibited by the critical solution and compare these results with values obtained from analytic perturbative mode analysis of equilibrium TOV configurations. We also establish the universality of the critical solution with respect to a 1-parameter family of initial data as well as the phase space manifold of the critical collapse.
MONA: The Munich Motion Dataset of Natural Driving

Real-world datasets facilitate the development of autonomous vehicles, especially when they are accessible, diverse, and provide a measure of accuracy. While existing datasets have been accessible and diverse, they cannot provide any measure of accuracy. To estimate the accuracy of the detection of traffic participants in our setup, we repetitively drove through our observation area with a measurement vehicle with highly accurate localization and LiDAR sensors. Our experiments showed an average overall position accuracy of 0.51 m. The combined data of the autonomous vehicle and the elevated camera setup yield a unique dataset. The elevated view acts as a super sensor of the autonomous vehicle with extended range and reduced occlusions. We employ an auto-labeling system on the stationary camera data to extract trajectories with bounding boxes for each traffic participant. These extracted trajectories are smoothed for kinematic feasibility, and corresponding maps for each location are provided. The Munich Motion Dataset of Natural Driving (MONA) shall empower new research in prediction and planning. Making raw data and code available to the public without license restrictions allows the dataset to be further improved using more advanced algorithms.
Modeling Human Driver Behaviors When Following Autonomous Vehicles: An Inverse Reinforcement Learning Approach

During the transition period, the interactions between human-driven vehicles (HVs) and autonomous vehicles (AVs), especially the car-following behaviors, need to be analyzed comprehensively to provide feedbacks to AV controllers, increase the inference ability of AVs and reflect the social acceptance of AVs. Previous studies have found that HVs behave differently when following AVs compared to when following HVs through traffic/numerical simulations or field experiments. However, these works have critical drawbacks such as simplified driving environments and limited sample sizes. The objective of this study is to realistically model and understand HV-following-AV dynamics and their microscopic interactions. An inverse reinforcement learning model (Inverse soft-Q Learning) has been implemented to retrieve HVs' reward functions in HV-following-AV events. Then a deep reinforcement learning (DRL) approach ― soft actor-critic (SAC) is adopted to estimate the optimal policy for HVs following AVs. HV-following-AV events from the high-resolution (10 Hz) Waymo Open Dataset are extracted to validate the proposed model. The results show that compared with other conventional and data-driven car-following models, the proposed model leads to significantly more accurate trajectory predictions and gains more insights into HVs' car-following behaviors.
The Effect of Preprocessing Techniques on Stacked Generalization and Stand-Alone Method for E-commerce Demand Prediction

The competition between sellers of e-commerce in Indonesia are getting more intense. One of the ways for the sellers to stay competing in the industry is by managing their product inventory through demand prediction. A lot of research related to demand prediction had been conducted. On those research, preprocessing techniques were always implemented [16] [26] [33], but there hasn’t been a statement or a main guide related to the effect of preprocessing techniques on ecommerce demand prediction, like the research that was conducted on 2022 for steel industry. In this paper, multiple scenarios will be conducted to see the effects of preprocessing techniques on stacked generalization and stand-alone method using MLP (Multi Layer Perceptron) and XGBoost (Xtreme Gradient Boosting). It is found that data scaling can increase the performance of stacked generalization with MLP on level 1. The training time will also be faster if there is a significant improvement from highly skewed to normal distribution data. With feature interaction constraint, the training time of stacked generalization and stand-alone method can become faster with the same or better prediction performance, if the constraint is in accordance with the problem. Stacked generalization, especially with XGBoost on level 1, can result in a better prediction than stand-alone MLP with hyperparameter tuning and data scaling. If the data doesn’t have a high skewness, stacked generalization method can minimalize the error made by unsuitable parameter in stand-alone XGBoost.
Background Extraction and Objects Segmentation with 3D Roadside LiDAR under Snowy Weather

This paper proposes a new method to extract background and segment targets from point clouds collected by three-dimensional roadside LiDAR in snowfall weather. Background point extraction and target segmentation are two main problems in environmental perception based on roadside LiDAR. This paper first introduces a new background filtering algorithm, which uses the historical point cloud sequence to construct the background model in real time and filter the background by background difference. Then, a non-background target segmentation algorithm is proposed, including a linear density filter for filtering snow noise and a hierarchical target clustering algorithm at the beam level, which effectively realizes the accurate detection of road targets affected by snow occlusion and avoids over-segmentation and under-segmentation. Finally, the performance of our method is compared with that of the commonly used background extraction and target segmentation methods by using the data collected in the snowfall environment by our intelligent roadside system. The results show that the target detection accuracy rate and recall rate of our method can reach 95.5% and 96.7%, respectively, which are better than those of the existing methods, and can be more accurate and effective to segment road targets from the snowfall cloud.
Modeling and Managing an On-Demand Meal Delivery System with Mixed Autonomy

This paper investigates the on-demand meal delivery system with mixed autonomy. We have explored how the future implementation of autonomous vehicles (AVs) in the system will affects demand, the labor market of human couriers (HCs), and the service provider in the system. In the system, the service provider determines the fleet size of AVs, the average delivery price for customers, and the average hourly wage for HCs. In response to the operation and pricing strategies, customers decide whether or not to order meals with delivery services, and potential HCs decide whether or not to work for the system. Therefore, a market model is proposed to capture the interactions among the service provider, customers, and HCs. An adaptive particle swarm optimization (APSO) algorithm is adopted to find optimal solutions. The results of numerical experiments show that a lower cost of AVs leads to higher penetration of AVs, lowered delivery price, and improved service quality. As a result, expanded demand is expected. By comparing the market outcomes under a varying number of potential customers, we find that AVs are considered more cost-efficient in densely populated areas than HCs, and have a higher percentage in the mixed fleet. Henee, customers in those areas are served with improved quality of delivery services.
Bearing Fault Diagnosis Based on Wavelet Transform and Residual Shrinkage Network

Rolling bearings are frequently employed in machinery equipment, and the safely production of such equipment depend greatly on the properly work of bearing. Fault diagnosis of bearing is usually performed by means of vibration signal analysis, which are further divided into traditional analysis methods and machine learning based analysis methods. Traditional methods rely on expert knowledge, which makes it difficult to extract fault features, machine learning methods rely on huge volumes of data and need to eliminate the distraction of surrounding noise. This paper proposes a fault diagnosis method based on wavelet transform and deep residual shrinkage network. Firstly, transform the vibration signal into wavelet time-frequency map by wavelet transform method, and then expand data set by augment to create enough training samples, next, build a deep residual shrinkage network, and train the model for fault classification, thus implement the fault diagnosis task. Experiment shows that the accuracy of this model is superior to other comparable models, which reached 99.3% in low-noise environment, and 93.6% in high-noise environment.
MAGPIS: A Multi-Array Galactic Plane Imaging Survey

We present the Multi-Array Galactic Plane Imaging Survey (MAGPIS), which maps portions of the first Galactic quadrant with an angular resolution, sensitivity, and dynamic range that surpass existing radio images of the Milky Way by more than an order of magnitude. The source detection threshold at 20 cm is in the range 1-2 mJy over the 85% of the survey region (5° < l < 32°, |b| < 0.°8) not covered by bright extended emission; the angular resolution is ∼6″. We catalog over 3000 discrete sources (diameters mostly <30″) and present an atlas of ∼400 diffuse emission regions. New and archival data at 90 cm for the whole survey area are also presented. Comparison of our catalogs and images with the Midcourse Space Experiment mid-infrared data allows us to provide preliminary discrimination between thermal and nonthermal sources. We identify 49 high-probability supernova remnant candidates, increasing by a factor of 7 the number of known remnants with diameters smaller than 5′ in the survey region; several are pulsar wind nebula candidates and/or very small diameter remnants (D < 45″). We report the tentative identification of several hundred H II regions based on a comparison with the mid-IR data; they range in size from unresolved ultracompact sources to large complexes of diffuse emission on scales of half a degree. In several of the latter regions, cospatial nonthermal emission illustrates the interplay between stellar death and birth. We comment briefly on plans for follow-up observations and our extension of the survey; when complemented by data from ongoing X-ray and mid-IR observations, we expect MAGPIS to provide an important contribution to our understanding of the birth and death of massive stars in the Milky Way.
The Impact of Big Data Analytics on Traffic Prediction

The Internet of Vehicles (IoVs) performs the rapid expansion of connected devices. This massive number of devices is constantly generating a massive and near-real-time data stream for numerous applications, which is known as big data. Analyzing such big data to find, predict, and control decisions is a critical solution for IoVs to enhance service quality and experience. Thus, the main goal of this paper is to study the impact of big data analytics on traffic prediction in IoVs. In which we have used big data analytics steps to predict the traffic flow, and based on different deep neural models such as LSTM, CNN-LSTM, and GRU. The models are validated using evaluation metrics, MAE, MSE, RMSE, and R2. Hence, a case study based on a real-world road is used to implement and test the efficiency of the traffic prediction models.
Cholula: Fast, Fault-tolerant, and Strongly Consistent Off-chain Object Storage

Emerging security technologies such as Blockchain are prominent to secure data. Blockchain inherits the advanced principles of cryptography and decentralization. This paper explores an appropriate design for distributed object storage systems for off-chain data. Our system Cholula has the following salient features: (i) Strong consistency; (ii) Optimal fast-path latency (1 RTT when there is no conflicting write); (iii) Tolerance of Byzantine servers; (iv) Security (preventing from an imposter attack); and (v) Censorship-resistance. We present our design and evaluation in this work. We demonstrate that Cholula has a better performance than the state-of-the-art object storage system Giza (the system behind Microsoft OneDrive) and Cassandra in the context of geo-replicated off-chain data.
EAFMNet: An Efficient Attention Fusion Mechanism for Lane Detection

Identifying and locating lane in the traffic environment is an important task for intelligent vehicle environment sensing system. Due to complex road environments, the lane detection task is still challenging. Because convolutional neural network (CNN) has the characteristic of local perception and lacks the ability to model the global context of the feature map, it's still hard for ordinary CNN to detect lane in complex traffic environment. In this study, we propose a fresh approach to detect lane base on an Efficient Attention Fusion Mechanism (EAFM). The main innovation of the model includes two aspects. Firstly, a Multi-scales Coarse Feature Extraction module (MCFE) is proposed to guide the feature learning of deep network by extracting rough feature maps at multiple scales; Secondly, we propose the EAFM to enhance the lane features and global information, so as to refine the result of lane detection. On Tusimple and CULane benchmark datasets, our model gets SOTA results.
Dynamic Channel Assignments for Efficient Use of Aviation Spectrum Allocations

The demand for voice and data communications continues to rise with the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations throughout the National Airspace System (NAS). Recent studies have shown that the anticipated growing demand for spectrum resources will exceed the capacity of existing aviation spectrum allocations. Further, airspace configurations, via assignment of fixed channel allocations within standard service volumes, do not allow for the dynamic and efficient distribution of spectrum resources based on airspace demand; as a result, a new approach to aviation spectrum management is needed to support the forecasted needs of new airspace users. The National Aeronautics and Space Administration (NASA) is investigating applications of artificial intelligence (AI), machine learning (ML), and other advanced concepts to solve a dynamic constraint satisfaction problem which is analogous to the frequency assignment problem faced by aviation. Procedures and strategies for dynamic channel allocation can be borrowed from other large-scale mobile services (i.e., 4G/5G applications) and can provide a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace while maintaining the strict quality of service prescribed by aeronautical standards.