Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Broadband Millimeter-Wave 2-Stage Power Amplifier Design in 40-nm GaN pHEMTs

We present two design examples with measurement data to highlight the benefits of using a stacked topology over a common source (CS) topology for broadband millimeter-wave (mm-Wave) power amplifier (PA) design. These medium power PAs are designed using a low-voltage state-of-the-art 40-nm gallium nitride (GaN) pseudo-high-electron-mobility transistor (pHEMT) process. Two PAs are designed and fabricated, one using a CS driver with another CS second stage (i.e., CS/CS PA), and the other using a CS driver and a 2-stack second stage (i.e., CS/2-stack PA). Their measurement data is compared to post-layout extracted (PEX) electromagnetic (EM) simulations, and also with other state-of-the-art designs found in literature. As expected, the CS/2-stack PA can achieve considerably higher power gain than a similar 2-stage CS/CS PA with similar power-added-efficiency (PAE). Good agreement on measured vs. PEX EM simulated results have been observed. However, performance degradation was discovered in measurement for high drain bias voltage not shown in simulations. Measurements taken on the CS/CS PA at 37/41/44 GHz yielded saturated output power (POUT,SAT) of 19.7/18.9/19.1 dBm with gain > 13 dB, with 3-dB bandwidth (BW) of ~ 21% from 37 – 45.6 GHz On the other hand, the CS/2-stack PA attained measured POUT,SAT of 23.2/24.6/23.2/23.3 dBm with over 20 dB gain at 21/24/28/31 GHz, and with a much wider 3-dB BW (i.e., ~ 55% from 19.9 – 35.1 GHz). As the DC bias voltage, VDD, on the 2-stack PA increased from 20 V to 24 V, performance degradation was observed in measurement repeatedly, even though the POUT,SAT is still only less than half a Watt. Therefore, we need to be more conscious of the DC breakdown limits when utilizing these low-voltage GaN transistors for mm-Wave PA design.
Weighted Feature detection Mechanism for Internet of Vehicles over Heterogeneous Vehicular Network

The current feature detection method for big data in the Internet of Vehicles (IoV) does not consider the weight of the data attribute, resulting in its inefficiency and inability to provide satisfactory vehicle operation services. A hybrid attribute-feature detection method is proposed for IoV big data in a multi-source, heterogeneous environment to address this issue. This method utilizes an integrated model to combine data from various sources in the Internet of Vehicles, which is then standardized and reduces the number of attributes. The weighted principal component analysis method is used to extract attribute characteristics from the combined data, and the clustering method is employed for aspect clustering. This comprehensive approach completes the mixed attribute feature detection of big data in the Internet of Vehicles. Experiment results indicate that the proposed method outperforms existing methods in sensitivity evaluation index, requires less time, and is faster in completing the feature extraction task from the mix of attributes in big data in the Internet of Vehicles.
Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis

Abstract Recently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN). The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN) and multi-layer perceptron neural network (MLP). Based on the results of the research, the efficiency of the developed algorithm can be inferred.
A Fairness Approach to Mitigating Racial Bias of Credit Scoring Models by Decision Tree and the Reweighing Fairness Algorithm

Credit scoring models have been widely applied by financial institutions, Peer to Peer (P2P) lending service providers, and Buy Now Pay Later (BNPL) service providers to evaluate their customers’ financial status. Therefore, it has a large impact on consumer financing activities. However, unfair evaluation may occur as the development of credit scoring models contains biased judgments (e.g., racial bias), which deteriorates users’ credit access ability. Thus, we study the feasibility of mitigating racial bias in developing a credit scoring model. By using a data set provided by a P2P lending platform, LendingClub, we integrated the C5.0 decision tree algorithm and the reweighing fairness algorithm to develop credit scoring models with cost-sensitive modeling concepts. Multi-class fair credit scoring evaluation was also studied in terms of performance indices, including accuracy, average cost, and unfairness metrics. The results demonstrated that the reweighing fairness algorithm reduced the unfairness and average cost of models. In addition, combining the fairness algorithm and cost-sensitive modeling minimized the average cost of models while maintaining the functionality of the fairness algorithm.
Evaluation of Photovoltaic Utilization Potential of Urban Building Roof

The evaluation method and step of photovoltaic utilization potential of urban buildings roof are put forward based on a three-step method: Firstly, the roof of the building was identified according to the improved SegNeXt and GIS, and then the roof was reduced according to the provisions of the national Code for Design of Photovoltaic Power Station GB50797-2012. Finally, the photovoltaic utilization potential of the roof was calculated based on this. This method has low cost, high precision and is suitable for large-scale urban buildings.
Ground Plane Segmentation and Outliers Removal in Point Clouds of Electrical Substations

With the increasing popularity of 3D laser scanning technology, automatic modeling of 3D point clouds of substation scenes is crucial for applications such like the digitalization of electrical {substations. However, the point clouds obtained by the 3D laser scanner is noisy. In addition to the equipment that needs to be extracted, it also contains ground plane data, vegetation data, power lines, and others. In this paper, we propose a ground plane segmentation and outliers removal method as a fundamental step toward substation entity modeling. In particular, we have developed an automatic ground plane removal method and an interactive method for removing outliers. Finally, the effectiveness of the model proposed in this paper is validated through a case study in a substation point cloud.
Hexahydrocurcumin mitigates angiotensin II-induced proliferation, migration, and inflammation in vascular smooth muscle cells

The proliferation and migration of vascular smooth muscle cells (VSMCs) play vital roles in the pathogenesis of atherosclerosis and hypertension. It has been proposed and verified that hexahydrocurcumin (HHC), a metabolite form of curcumin, has cardiovascular protective effects. This study examined the effect of HHC on angiotensin II (Ang II)-induced proliferation, migration, and inflammation in rat aortic VSMCs and explored the molecular mechanisms related to the processes. The results showed that HHC significantly suppressed Ang II-induced proliferation, migration, and inflammation in VSMCs. HHC inhibited Ang II-induction of the increase in cyclin D1 and decrease in p21 expression in VSMCs. Moreover, HHC attenuated the generation of reactive oxygen species (ROS), and the expression of nuclear factor kappa B (NF-κB), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and matrix metalloproteinases-9 (MMP9) in Ang II-induced VSMCs. The proliferation, migration, inflammation, and ROS production were also inhibited by GKT137831 (NADPH oxidase, NOX1/4 inhibitor) and the combination of HHC and GKT137831. In addition, HHC restored the Ang-II inhibited expression of peroxisome proliferator-activated receptor-γ (PPAR-γ) and peroxisome proliferator activated receptor-γ coactivator-1α (PGC-1α). These findings indicate that HHC may play a protective role in Ang II-promoted proliferation, migration, and inflammation by suppressing NADPH oxidase mediated ROS generation and elevating PPAR-γ and PGC-1α expression. See also Figure 1(Fig. 1).
A Lightweight Convolutional Neural Network for Atrial Fibrillation Detection Using Dual-Channel Binary Features from Single-Lead Short ECG

Atrial fibrillation (AF) is a prevalent cardiovascular disease in the elderly, significantly increasing the risk of stroke and heart failure, etc. While the artificial neural network (ANN) has recently demonstrated high accuracy in ECG-based AF detection, its high computation complexity makes it challenging for real-time and long-term monitoring on low-power wearable devices, which is critical for detecting paroxysmal AF. Therefore, in this work, a lightweight convolutional neural network for AF detection is proposed using a dual-channel binary features extraction technique from single-lead short ECG to achieve both high classification accuracy and low computation complexity, and evaluated on the 2017 PhysioNet/CinC Challenge dataset, the proposed method achieves 93.6% sensitivity and 0.81 F1 score for AF detection. Moreover, this design consumes only 1.83M parameters, achieving up to 27x reductions compared with prior works, and only needs 57M MACs for calculation. As a result, it is suitable for deployment in low-power wearable devices for long-term AF monitoring.
Recovering CSI and Data in Dense Network Environments Using IEEE 802.11ax Midamble

There has been considerable amount of research to improve performance for a dense wireless local area network (WLAN) environment. While some studies focus on aggressive channel access, others highlight frequent channel state information (CSI) corruption by excessive transmissions. We pay attention to midamble, adopted in the IEEE 802.11ax standard specification, to overcome CSI corruption. If the transmitter inserts midamble symbols, the receiver can acquire new CSI periodically during a physical layer convergence protocol (PLCP) protocol data unit (PPDU) frame. In this paper, we propose a precise receiver performance model to describe the impact of CSI, and design a standard-compliant algorithm called REMEDY that works with conventional channel access schemes by handling CSI corruption and time-varying channels in high-density WLAN networks. REMEDY determines whether to use midamble considering overhead, estimates on-frame signal-to-interference and noise ratio (SINR) to notice channel environments, and cancels the effects of scrambling and incorrect descrambling. We evaluate the performance of the conventional schemes with and without REMEDY in 11ax task group (TGax) indoor simulation scenarios using the ns-3 simulator, considering time-varying channels and CSI corruption. REMEDY helps the existing channel access schemes to achieve up to $2.20\times $ higher throughput while improving the throughput of the lowest performing group, compared to the existing schemes without REMEDY.
Simple Industrial Cutting Machine Safety System Based on Computer Vision

Safety devices are often not fitted to many simple panel-cutting machines due to cost. Designing and installing low-cost safety devices on the simple panel-cutting machine improves the safety of operators. We propose a computer vision-based safety system with a camera to capture images of the operator and machine from above. Each captured image is analyzed by a neural network. If the operator's hands are too close to the cutting tool, the safety system sends a warning signal and stops the machine. The neural network used in the vision system is a ten-layer convolutional neural network. In addition to the input and output layers, there are five convolutional layers in the middle, followed by three fully connected layers. The input image undergoes five convolution operations to obtain feature maps of different granularity. With three fully connected layers, a combination of features is used to determine the positions of the operator's hands as a guideline for stopping the machine. To improve the generalization of the model, we collect thousands of training data. The experimental results show that the model has a low bias, low variance, and high accuracy characteristics. The accuracy of the verification data is over 98%.
Classification of Cardiac Arrhythmia Using Machine Learning Algorithms

Irregular and unexpected slow or fast heartbeat is called arrhythmia. The two lower chambers of the heart, known as the ventricles, are where an irregular heart rhythm called a ventricular arrhythmia (VA) originates same in the case of atrial fibrillation which occurs in the two upper chambers called as aorta. An arrhythmia is a dangerous medical condition that needs prompt attention. An electrocardiogram or monitor is typically needed for its diagnosis in order to capture the irregular heart rhythms. If arrhythmia is the cause of cardiac arrest, the electrocardiogram (ECG) can help identify the cause. In this research, to investigate and evaluate a few pre-processing techniques for ECG signals. As the initial stage of pre-processing, this involves a variety of techniques to eliminate noise from the ECG signals and any undesired disturbances like Equi-ripple notch filter and Kaiser Window function. Next, several ways were investigated to turn ECG signals into pictures, segments, or waveforms that can be used as input for various machine learning algorithms like Logistic regression, Random Forest, Decision Tree and KNN classifier to analyze and forecast the likelihood of ventricular arrhythmia. Finally, a comparative analysis is done to compare all the machine learning techniques using multiple pre-processing techniques.
Networking for Democratization of Data Ownership

Web3, decentralized web, is one of the buzz words brought up in the field of IT business as well as IT technology. Data ownership is supposed to be democratized by Web3, which means data is kept at its owner and the availability of the data is controlled and managed by the owner in the world of Web3. One of the mechanisms providing democratization of data ownership is InterPlanetary File System (IPFS) which leaves data as well as its meta data at its owner and only pointers to data are distributed over the network for data sharing. This paper discusses network layer support for IPFS to improve its performance. In particular, we propose to use Information Centric Networking as the network layer of IPFS to speed up data search in IPFS.
Power Maximization Strategy of Wind Farm Considering Torque Gain Coefficient Collaborative Optimization

Due to the existence of complex wake effect in wind farms, the wind speed faced by wind turbines at different locations in the wind farm is significantly different. In this case, if each wind turbine still adopts the traditional maximum power point tracking (MPPT) strategy to maximize its capture efficiency, and ignores the wake coupling characteristics among different wind turbines, the overall captured power of the wind farm will be reduced. In this regard, this paper first describes the problems of the existing MPPT improvement strategy based on torque gain reduction for individual wind turbines applied in wind farms, and then proposes a wind farm-level power maximization strategy considering the torque gain coefficient collaborative optimization. First of all, a large number of sample data of the wind farm can be obtained quickly and accurately through the slime mould algorithm (SMA), and then combined with the convolutional neural network (CNN) to build the mapping relationship among the incoming wind speed characteristics, wind turbine position and torque gain coefficient, so that during the operation of the wind farm, the torque gain coefficient of each wind turbine can be dynamically optimized to maximize the total power. Finally, based on the professional wind farm simulation software SimWindFarm, the feasibility and superiority of the proposed method in the wake wind farm are verified.
Enhanced Inpainting Model Revitalizes Historical Paintings with Vision Transformer

This paper presents a deep learning architecture for restoring ancient paintings, which have immense historical and artistic value as they vividly record history from diverse perspectives. Due to the passage of time, many historical works have suffered damage, which requires time-consuming manual restoration by skilled professionals. Our proposed method utilizes a sophisticated edge detection model to extract structure information from the paintings, including texture, painting style, and structure, which are applied for restoration. The effectiveness of the proposed method was validated by training and testing on various ancient painting datasets. This work has significant value in that it can expedite and enhance the accuracy of the restoration process without compromising the original artistic style and intent, thereby better preserving and transmitting our historical culture. We believe that the contribution of this work is meaningful for VR cultural heritage conservation and presentation.
Small Target Forest Fire Recognition Method based on Deep Learning

Due to the small size of the fire target in the early stage of the forest fire, the model has the problem of missing detection of the small target forest fire. This paper proposes a forest fire recognition method based on YOLOv5 small target detection. In order to obtain flame space information at different scales and obtain an output containing abstract feature information, replace the SPP module in the YOLOv5 backbone network with the WASP module; in order to enhance the expression of flame features, add the Triplet attention module to the C3 module; In order to improve the detection effect of the detection algorithm on small flames, a small target detection layer is added. In this paper, a set of ablation experiments and a set of comparative experiments are designed to verify the effectiveness of the proposed algorithm. The experimental results show that compared with the original YOLOv5 algorithm, the algorithm in this paper has improved mAP@50 by 3.49%, and can accurately detect small target forest fire targets, effectively improving the ability to prevent forest fires.
Adaptive Lighting Modeling in 3D Reconstruction with Illumination Properties Recovery

In 3D reconstruction, appearance is always overlooked compared to geometry and motion. The existing methods prefer texture-based shading, where the reconstructed appearance cannot perform re-lighting, thus weakens the authenticity, aesthetic and availability. Although excellent attempts were made in some works at lighting model-based shading in 3D reconstruction, still they fall short of differentiated rendering of materials. In this paper, we propose a method that can make an artistic effect on the recovery of illumination properties in 3D reconstruction from a single RGB-D image. Our method builds adaptive lighting models for different surfaces, which are used in re-lighting and recovering surface properties. We start with the establishment of coordinate system conversion tables for rapidly matching surface parameters and location. Then, a projection-based triangulation method is developed to generate polygonal mesh. After that, we propose a physically based lighting model with few variable parameters to recover surface illumination properties, in order to accurately describe the reflection of light from different materials. Furthermore, we develop an adaptive algorithm for generating parameters. Since the algorithm is based on the surface consistency of albedo over channels and training-less also, it is applicable to a surface of any shape and material, and the physically based lighting model brings a more vivid re-lighting. The obtained experimental results demonstrate that our method could achieve an excellent reconstructed appearance in both artistic view and verisimilitude.
Non-smooth High-precision Control of Drag-free Satellite for Space Gravitational Wave Detection

In this paper, the control mode and working requirements of the drag-free system are introduced. The dynamic model of drag-free satellite with single mass is established along the direction of the sensitive axis in the displacement mode, and the non-conservative forces acting on the satellite platform are analyzed. A continuous time-invariant non-smooth nonlinear state feedback control law is designed for the drag-free system with double integral characteristics, and the stability of the closed-loop system is proved based on Lyaponuv theory. The simulation results show that the non-smooth nonlinear control strategy similar to PD control can ensure the ultra-low bandwidth requirement proposed by the space gravitational wave detection mission while satisfying the high-precision control index requirement for the relative displacement and the residual disturbance acceleration of drag-free satellite platform.
Smartphone Malware Detection using Permissions and McNemar test

A recent report has shown that the availability of smartphones is increasing at an alarming rate and hence the number of mobile malware is exponentially increasing with the increase in popularity of smartphones. Looking at the level of threat from malware applications for Android users, it becomes essential to detect malware applications in a quick and effective way. One such way is to use permissions. To make an effective system for malware detection using permissions, a large dataset and different permissions are required to analyze the pattern. With a large number of permissions for analysis, the time of computation increases drastically. The time of computation can be reduced if the number of datasets or the number of permissions gets reduced. Reducing the number of features is preferred over decreasing the number of datasets. Further, the number of permissions can be rduced only if the permissions that are most distinguishing are selected by ignoring the permissions that don’t play a huge role in distinguishing between malware and benign applications. Thus, a novel method is required to rank the permissions based on how well that permission can be used to detect the nature of the application. This study introduces a statistical technique named McNemar test to find the correlation of a set of permissions with malware and benign applications and rank the permissions. The correlation gives a numerical value for the overlapping of each permission in malware and benign applications. The greater the correlation value lesser will be its usefulness in distinguishing the nature of the application. Such ranking helps us eliminate irrelevant permissions. This ranking can be further used for detection using various machine-learning algorithms. As a result, this study has narrowed down the total set of permissions from 129 to 38 and got 97% detection accuracy with the Random Forest classifier.