Miracle-dz/UniFAR-FTUs huggingface dataset:
A Proposed Intelligent Adaptive Controllers for Wind Turbine Driving DFIG for Improving the Output Generated Power

Renewable energy resources are considered as an important topic these days and in the near future due to the climate changes and environmental issues. Wind energy is promising to reduce the production of CO2 emission. The wind power is fluctuating and intermittent due to the wind speed fluctuation. Adjusting the generated output power is not an easy issue unless we have an intelligent system. This work is proposing intelligent adaptive controllers to improve the quality of the wind output power generated. The proposed controllers are of proportional integrals types, but they are intelligent and adaptable. They take their signals from the wind speed, wind power generated and the required nominal wind output power. Depending on these signals the controllers parameters are adjusted to improve the quality of the generated output power. These controllers are tested using Simulink/Matlab. The results prove the effectiveness of the proposed controllers in improving the generated output wind power. The wind turbine with these proposed controllers could work under different severe conditions.
Mobility Enabled Security for Optimizing IoT based Intelligent Applications

The critical challenges for IoT-based intelligent applications for the adaptive environment are effective resource allocation, secure data transmission, and mobility management. It is highly significant that these mandatory requirements be considered in the overall IoT-enabled smart networks. In this research, we initially propose the resource allocation model for IoT-based systems by adopting the security, energy drain, and cost factors. Furthermore, MMSA, incorporating security with mobility, is developed. The experimental analysis demonstrates that the proposed MMSA outperforms conventional techniques in terms of mobility management, delay, security, and criticality factors with optimal resource allocation. The findings of this research demonstrate that the security level, energy optimization, and reliability for the proposed MMSA, traditional, and baseline methods are (15 percent, 13 percent, 11 percent), (20mJ, 14mJ, and 9.5mJ), and (9.78 percent, 6.8 percent, and 5.6 percent), respectively. Consequently, it can be concluded that the proposed approach can be widely used for intelligent IoT systems because it delivers better reliability and mobility-enabled security than its counterparts.
An RF-Powered IoT Node for Environment Sensoring

The widespread deployment of the Internet of Things requires new embedded systems providing functionality with extremely low power consumption. This paper presents a battery-less, low-power consumption, compact embedded system using remote RF powering with COTS (Components Off The Shelf), and OOK or ASK based data communications, operating in the 900 MHz band.
Overlapping Community Detection in Weighted Temporal Text Networks

Network is a powerful language to represent relational data. One way to understand network is to analyze groups of nodes which share same properties or functions. The task of discovering such groups is known as community detection. The community detection in real-life networks, the majority of which are weighted temporal text networks, is confronted with two main problems – how to model the weight of edges and how to exploit the temporal information. Existing works either ignore the edge weight or utilize it in graph measures like modularity, which lacks scalability. And currently the common-used method involving temporal information is to discretize the time, which leads to series of problems. We are thus motivated to present a new method to encode the edge weight and temporal information. A probabilistic generative model, named Custom Temporal Community Detection (CTCD) is introduced, which views the link between two nodes as a weighted edge with several time stamps. Our model utilizes network, semantic and temporal information simultaneously to extract temporal community affiliations for individual user, influence strength across communities and temporal interested topic in each community. An efficient inference method, which scales linearly, and corresponding parallel implementation are proposed to adapt to large datasets. Through the knowledge extracted by CTCD, we are able to spot the community shift of the individual user, to which little attention has been given, and employ it to track the development of the communities over time. Moreover, experiments on two large-scale weighted temporal text networks show that CTCD gains significant improvement over state-of-the-art methods on a series of tasks.
Doherty PAs for 5G Massive MIMO: Energy-Efficient Integrated DPA MMICs for Sub-6-GHz and mm-Wave 5G Massive MIMO Systems

To accommodate growing user demand for faster data rates and extensive connectivity, modern wireless communication systems must evolve to support a sharply increasing number of subscribers, all requesting service at the same time. This trend encourages the broad application of multiple input/multiple output (MIMO) systems. In fact, MIMO techniques can increase data rates, coverage of service areas, and communication reliability without additional RFs. In recent proposals for 5G systems, the required separate RF chains in massive MIMO RF front ends can reach up to 256, with bandwidths of up to 800 MHz per RF chain [1], [2]. Massive MIMO is a critical technology that helps significantly in increasing network capacity and spectral efficiency, while reducing wireless network interference, ultimately improving the end-user experience.
Conditionally Optimal Task Parallelization for Global EDF on Multi-core Systems

Targeting global EDF scheduling, this paper proposes a conditionally optimal algorithm for parallelizing tasks with parallelization freedom. For this, we extend the interference-based sufficient schedulability analysis and derive monotonic increasing properties of both tolerance and interference for the schedulability. Leveraging those properties, we propose a one-way search based conditionally optimal algorithm with polynomial time complexity. Our extensive experiments through both simulation and actual implementation show that our proposed approach can significantly improve the schedulability up to 60 percent.
Downlink Power Optimization for Cell-Free Massive MIMO Over Spatially Correlated Rayleigh Fading Channels

Cell-free massive multiple-input multiple-output (mMIMO) is regarded as a promising technology in the future wireless communication. This paper aims to minimize the downlink power consumption of cell-free mMIMO system under the downlink rate constraints of users and the power constraints of per-antenna over spatially correlated Rayleigh fading channels. Firstly, this paper studies the performance of two different downlink transmission modes: non-coherent joint transmission and coherent joint transmission. Then, for the two transmission modes, the corresponding downlink power optimization model is established and an efficient power optimization algorithm is proposed based on the Lagrange multiplier (LM) method. Under the power optimization model, this paper analyzes the impact of different factors on the downlink power consumption of cell-free mMIMO system. Simulation results show that coherent joint transmission performs better than non-coherent joint transmission on spectral efficiency (SE) and energy efficiency (EE). In addition, under the same power control strategy, the downlink power consumption of cell-free mMIMO is much lower than co-located mMIMO. Moreover, the total transmission power decreases when more antennas are utilized at each access point (AP) or spatial correlation of channels becomes weak.
Fabrication of Single-Crystal Silicon Nanoslits with Feature Sizes Down to 4 nm and High Length-Width Ratios

In this paper we report firstly the realization of nanoslits with feature sizes down to sub-4 nm in single-crystal silicon substrate at chip scale. Three-step anisotropic wet etching (TSWE) method was used in the fabrication process and a low-noise current feedback experimental set-up was developed to monitor the slit-opening event in the last step. Individual nanoslit with 3.8 nm width and $3.88\ \mu \mathrm{m}$ length, which means the nanoslit with a length-width ratio more than 1000 was successfully obtained.
Graphene Magneto-Electric Antenna on Curved Structures for Millimeter Wave Communications

This paper introduces a complete study on the radiation characterstics of magneto-electric (ME) dipole antenna placed on curved surfaces. The ME-dipole antenna consists of graphene sheets printed on silicon oxide substrate and placed on ground plane cavity. The ME-dipole is proximity fed with Γ-strip feed in the cavity centre. Cylinderical and spherical shapes are studied with different radii of curvature. The effect of curvature on the reflection coefficient, gain, axial-ratio, and radiation efficiency is investigated. An array of ME-dipole antenna is designed on cylinderical surfaces and the radiation characterstics is investigated. The antenna structures are fully simulated using the finite integral technique.
Research on Massive Data Analysis of Power Utilization Based on Improved K-Means Algorithm and Cloud Computing

For such difficulties as low mining efficiency and large amount of data that the data mining of residential electricity data has to be faced with, the research on improved K-means algorithm and cloud computing-based analysis on massive data of power utilization is performed. Firstly, as the initial cluster center and the value K is difficult to determine in traditional K-means algorithm, an improved K-means algorithm based on density is proposed. Defining the product of sample density, the reciprocal of the average distance between the samples in the cluster, and the distance between the clusters as weight product, the initial center is determined successively according to the maximum weight product method and the accuracy of the clustering is improved. Then, the parallelization of improved K-means algorithm based on MapReduce model is realized and the efficiency of clustering is improved. Finally, the mining experiment of massive power utilization data is carried out on basis of 400 households' electricity data. Taking family as unit, calculate such features as electricity consumption rate during peak hour, load rate, valley load coefficient and the percentage of power using during normal hour, and establish the data vector dimension, completing the clustering of similar user types, while analyzing the behavioral characteristics of each types of users. Experiment results on Hadoop cluster show that the improved algorithm can be stable and efficient operated and it can achieve better clustering effect.
Design Optimization of a Lorentz Force, Amplitude Modulated, MEMS Space Magnetometer

This paper reports a MEMS magnetometer with geometrical improvements to increase the displacement resulting from Lorentz forces. It relies on lever-like suspension springs which present over tenfold lower electrical resistance (thus allowing higher currents) than conventional guided-beam springs with the same compliance. By combining multiple Lorentz force generating bars and off-resonance operation in vacuum (1 mbar), these devices can yield: noise of 2.65 nT/√Hz (theoretical) with bias current of 4 mA in each of the 6 Lorentz bars (63.6 nT.mA/√Hz) and sensitivity of $50.5\ \mathrm{aF}/\mu \mathrm{T} (1\ \mathrm{aF}.\mu \mathrm{T}^{-1}.\mathrm{mA}^{-1};\ 102\ \mu \mathrm{V}/\mu \mathrm{T}$ with the electronic readout), corresponding to a 20 nT resolution (1 aF capacitance variation), over a 50 Hz bandwidth. This resolution figure over such large bandwidth is unprecedented whether for amplitude-modulated or frequency-modulated Lorentz force magnetometers.
Electric Vehicles as Mobile Energy Storage Devices to Alleviate Network Congestion

Electric vehicles (EVs) usage is becoming ubiquitous nowadays. Widespread integration of electric vehicles into electric energy distribution systems (EEDSs) has a twofold impact: (1) It may impose a burden on EEDSs when EVs are charging, (2) EVs could discharge their battery capacity using V2G technology when required. To mitigate adverse effects of massive integration of EVs in EEDSs, EVs could be used as mobile energy storage devices (MESDs) to transfer electric energy throughout EEDSs using a proper charging/discharging scheme. In this paper, a mixed integer linear programming (MILP) model is proposed to control charging and discharging of EVs to improve EEDS performance. EVs are modeled as MESDs and their impact on EEDS is investigated. The proposed model is applied to 33-bus EEDS and Sioux Falls Transportation Network (TN). The results show that using this model, EVs could transfer electric energy to mitigate congestion in EEDS lines and improve performance of EEDSs.
Deep Learning-Based Object Detection Improvement for Tomato Disease

To improve the recognition model accuracy of crop disease leaves and locating diseased leaves, this paper proposes an improved Faster RCNN to detect healthy tomato leaves and four diseases: powdery mildew, blight, leaf mold fungus and ToMV. First, we use a depth residual network to replace VGG16 for image feature extraction so we can obtain deeper disease features. Second, the k-means clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset. Finally, we carry out a k-means experiment with three kinds of different feature extraction networks. The experimental results show that the improved method for crop leaf disease detection had 2.71% higher recognition accuracy and a faster detection speed than the original Faster RCNN.
Tongue Semantic Segmentation Based on Fully Convolutional Neural Network

It is difficult to accurately segment the tongue due to the blurred contours and the similar colors of the surrounding tissue. This paper presents a semantic segmentation method for tongue body based on fully convolutional neural network. First, the convolutional and pooling layers are used to map the tongue image from the pixel domain to the feature domain. Then, the deconvolutional layer is used to restore the shrinking feature image to the original size. Finally, the Softmax classifier is trained to classify each point of the image in order to achieve segmentation. Experimental results show that the method proposed in this paper can get better segmentation results, laying a solid foundation for tongue analysis and dialectical diagnosis.
High-Throughput Deep Learning Microscopy Using Multi-Angle Super-Resolution

Biomedical applications such as pathology and hematology expect microscopes with high space-bandwidth product (SBP) which is difficult to achieve with conventional microscope setup. By applying a deep neural network, we demonstrate a high space-bandwidth product microscopic technique termed multi-angle super-resolution microscopy (MASRM) to achieve high-resolution imaging with the low-magnification objective. We design a multiple-branch deep residual network which extracts high-frequency information and color information in obliquely-illuminated low-resolution input images and generates high-resolution output. To train our network, we build a well-registered dataset in which both low-resolution input and high-resolution target are real captured images. We carry out detailed experiments to demonstrate the effectiveness of MASRM and compare it with a computational imaging technique termed Fourier ptychographic microscopy (FPM). This data-driven technique unleashes the potential of traditional microscopes with low cost and has broad prospects in biomedical applications.
mmWave-NOMA-Based Low-Latency and High-Reliable Communications for Enhancement of V2X Services

The enhanced vehicle-to-everything (eV2X) systems have stricter constraints of latency and reliability than before. To meet the low latency and high reliability (LLHR) demands, the investigation of semi-persistent scheduling (SPS) is of great significance. For the first time, we integrate millimeter wave (mmWave) and non-orthogonal multiple access (NOMA) into SPS strategy. The proposed strategy includes the beam division and user clustering process, the power allocation process, and the vehicular user equipment (VUE) – resource block (RB) matching process. Firstly, the joint-optimization problem in terms of user scheduling indicators and power allocation factors is formulated to minimize the SPS period. This problem is then proved to be non-deterministic polynomial hard (NP-hard) by being reduced into a vertex coloring problem. To solve it with polynomial complexity, we divide it into two subproblems and calculate them by iteration. For one subproblem, the LLHR power control algorithm is proposed to solve the non-convex reliability-optimization problem, which can provide an evaluation indicator for the user scheduling. For the other subproblem, the beam division and VUE clustering algorithm is designed to reduce the complexity of the VUE-RB matching. After that, the matching problem with peer effects is solved by the proposed union-based matching algorithm. The analytical results on stability, convergence and computational complexity are presented, and simulation results show that the proposed SPS strategy outperforms the existing schemes.
Regulatory genetic variation in the Flavin-containing monooxygenase 1 gene (FMO1) : detection and functional consequences

The amount of both FMO1 mRNA and protein in various human tissues has previously been shown to vary by as much as 10-fold between individuals. This is likely to be caused by genetic variation as the enzyme‟s expression profile is not affected by exogenous agents. DNA sequences controlling transcription (promoter regions) have been defined upstream of the three transcriptional start sites P0, P1 and P2 within different cell lines. The use of these promoters has also been defined in various mouse tissues by real-time PCR. A number of SNPs are present within the three defined FMO1 promoters in addition to the 3′UTR. Sequencing of individuals from the Corriel repository consisting of individuals from Africa, Asia and Europe has revealed further variation including a CT deletion at the P1 transcriptional start site. To test the effect of genetic variation high-throughput DNase I capillary footprinting has been used to check for the effect of SNPs on DNA-protein binding. The technique has been shown to detect the effects of mutations on DNA-protein binding but no differences have been seen for FMO1 SNPs within promoter regions as yet. The technique has the potential to rapidly analyse regulatory polymorphism in a number of genes and the effect in different tissues without the need for cloning or cell culture. A SNP which introduces an additional in-frame upstream translation initiation codon has been tested in-vitro for its effect on translational efficiency. The upstream ATG variation has been shown to have a 2-fold increase in protein expression over the downstream ATG and therefore individuals with this SNP are likely to produce 2-fold more FMO1 protein resulting in different effects to drugs.
An approximate DRAM with efficient refresh schemes for low power deep learning applications

To avoid the accuracy drop caused by slowing down the refresh rate of a DRAM, the proposed approximate DRAM flexibly controls the refresh operation for different bits of data. Data are reorganized and mapped to different DRAM devices according to their bit significance. More critical bits are stored in more frequently refreshed devices while non-critical bits are stored in less frequently refreshed devices. Compared to the conventional DRAM, the proposed approximate DRAM requires only a separation of the chip select signal for each device in a DRAM rank and a minor change in the memory controller. Simulation results show that the refresh power consumption is reduced by 66.5 % with a negligible accuracy drop in computation for state-of-the-art deep networks.
Personalized Knee Geometry Modeling Based on Multi-Atlas Segmentation and Mesh Refinement

The development of personalized finite element models of the knee anatomy is critically important in the simulation of knee joint mechanics, prediction of optimal treatments in cases of pathological conditions and prevention of injuries. Subject-specific models can be obtained from diagnostic images with multi-atlas segmentation being a pertinent choice when prior anatomical information of the structures of interest is available. Although multi-atlas segmentation has been prevalent in some parts of the body, its exploitation for the segmentation of the knee complex has not been illustrated yet. This work utilizes a multi-atlas segmentation method based on deformable registration and joint label fusion in conjunction with anatomically-adopted mesh refinement in order to generate subject-specific models of the knee. The success of finite element simulations strongly depends on the properties of the 3D surface and the quality of the volumetric meshes. Therefore, emphasis was given to create structured meshes with well-shaped hexahedra for the knee cartilages and menisci. The segmentation performance is assessed using cross-validation on 7 subjects from the Open Knee project and 78 subjects from the Osteoarthritis Initiative. Our results indicate that our developed state-of-the-art processing scheme can achieve competitive performance, opening the path for better diagnostics and patient-specific interventions. The developed tools are freely available to download from SimTK at https://simtk.org/projects/knee-segment.
A High Power WPT System for through the Wall Applications

This paper presents a high-power wireless transfer system through a 200mm thick wall. This is applicable to IoT devices and 5G applications. The end to end efficiency is over 70% with 50W power. The transfer area is 50mm by 50mm with 270mm by 270mm coils The Class EF2 PA offers over 90% efficiency at 20ohm impedance. This system allows an outdoor unit (ODU) to be powered by an indoor unit without wire connection in the Air Fuel Alliance Resonant charging standards.
Analysis of grid inertial response with high penetration of PV systems and battery energy storage

The increasing incorporation of renewable energy sources with power electronics converters into electric power systems impacts in the grid inertial response. This impact is more severe when conventional generators are replaced for photovoltaic (PV) systems and when the penetration of this renewable source is high. In this paper, the impact of replacing synchronous generators for PV generation systems in a test network is analyzed. Due to PV generation systems cannot provide an inertial response, it is proposed to use a battery energy storage system (BESS) to enhance the grid inertial response under fault conditions. A comparative study is carried out through simulations in Simulink/MATLAB to show the grid inertial response of the test system, varying the number of synchronous generators, PV modules, and BESS.