Miracle-dz/UniFAR-FTUs huggingface dataset:
DOA Estimation Based on Average Processing of Redundant Virtual Array Elements for Coprime MIMO Radar

The redundant virtual array elements formed by the sum and difference coarray (SDCA) are always discarded in the existing DOA estimation methods, which could cause effective information loss and poor performance. In this paper, a new technology based on average processing of redundant virtual array elements is proposed for coprime MIMO radar direction of arrival (DOA) estimation. Then, Toeplitz matrix reconstruction based on MUSIC algorithm is employed to validate the effectiveness of the proposed technology for DOA estimation.
Hardness Reference Blocks as Certified Reference Materials to be supplied by Inmetro to the Market

Reference materials for the Rockwell, Brinell and Vickers hardness scales are fundamental for the Brazilian industrial sector. Mechanical property Hardness is the most common mechanical test used for quality control of raw materials in finished products as well as in research and development of new products and materials. A description of the main findings of two studies and the revalidation of the last on the demand of hardness reference blocks in Brazil is presented. This paper shows national demand of hardness reference blocks could be supplied on a large scale by Inmetro´s Force Laboratory.
A Method for Pulse Signal Denoising Based on VMD Parameter Optimization and Grey Wolf Optimizer

Aiming at the difficulty of parameter determination when using variational modal decomposition algorithm (VMD) to decompose photoplethysmographic(PPG) pulse wave signals, α VMD parameter optimization method based on Grey Wolf Optimizer (GWO) is proposed. In this paper, the envelope entropy of the K eigenmode component (IMF) after VMD decomposition is used as the fitness function, and the grey wolf optimization algorithm (GWO) is used to find the K sum corresponding to the minimum envelope entropy of the eigenmode component α, to determine the optimal value of the VMD algorithm parameters, and then use the parameter optimized VMD algorithm to filter out the noise in the PPG signal. The experimental results show that after decomposing the PPG signal using the algorithm in this paper, the effective component and the noise component of the signal are well separated. After selecting the appropriate IMF component for reconstruction, the noise in the PPG signal is effectively filtered out.
Changes in the footprint of grey water from 2005 to 2017 in Fuzhou City

Based on the concept of grey water footprint, this study quantified the pollutants generated by agricultural, industrial and tertiary industries in Fuzhou City from 2005 to 2017 using the diluted water demands. The results showed: 1) the mean agricultural grey water footprint of Fuzhou City was 15.88×108 m3/a at the study period, which is about 16.69% of the water consumption; 2) among three sectors, grey water footprint in the living sectorsis the largest, and agricultural grey water footprint is the smallest; 3) the grey water footprint of Fuzhou City shows a declining trend from 2005 to 2017, with the agricultural, industrial and living sectors decreasing by 24.92%, 60.78% and 17.11%, respectively; 4) the footprint of Fuzhou City’s grey water showed a downward trend, while the water quality in urban areas also showed a downward trend. The footprint of grey water is greater than the amount of water resources, implying the limited water resources in Fuzhou City can not fully dilute the pollutants for the environmental water quality standards.
Effect of different drying methods on the antioxidant properties of leaves of Centella asiatica

Centella asiatica is one of the vital plant sources of antioxidants properties for consumption or medicinal purposes. This study aimed to determine the effect of three different drying methods (freeze drying, oven drying and dehydration drying) on the antioxidant of leaves of C. asiatica. The antioxidant properties and IC50 value of different concentration of methanolic extract solution (0.5 mg/ml, 0.25 mg/ml, 0.125 mg/ml and 0.062 mg/ml), were evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. The drying conditions and time required for freeze drying, oven drying and dehydration drying were -45°C for 3 days, 60°C for 5 hours and 35-43°C within 1-3 hours, respectively. Samples with lower IC50 value showed a stronger antioxidant activity since low IC50 determine the efec. The freeze-dried of C. asiatica sample at 0.05 mg/ml concentration exhibited the highest scavenging activity (93.97 ± 0.45%) with the lowest IC50 value (0.05 mg/ml) whereas the lowest scavenging activity was shown by dehydration dried of C. asiatica sample at 0.05 mg/ml concentration (55.08 ± 0.03%) with the highest IC50 value (0.36 mg/ml). This indicates that as the concentration increases, the radical scavenging activity increases. Thus, it is highly recommended to use freeze drying as the method to conserve the maximum amount of antioxidant properties in C. asiatica leaves.
A Compact UWB Antenna Design for Breast Cancer detection

There are many diseases in the world. The mostcommon and dangerous disease is cancer and the number ofcancer patients is increasing and the number of deaths alsoincreased. If we detect the cancer at early stages, we can beable to reduce the deaths. In this paper, a rectangular fix receiving wire is intended to identify the disease at beginning phases and this increment the higher opportunity to recuperate from disease. The compact low profile wearable antenna is used to detect cancer. Microstrip patch antenna is used in this project because of lowprofile, low cost, miniaturization of antenna, high bandwidthand high gain. This antenna is compact because, IR can beeasily fabricated on the printed circuit board unlike otherantennas. The square fix is utilized to defeat the complex construction. The substrate utilized is FR4 epoxy with dielectric steady of 4.4 and digression deficiency of 0.02.
Point of Interest Recommendation Based on Graph Convolutional Neural Network

The rapid development of the mobile Internet makes location-based social networks (LBSNs) play an increasingly important role in practical applications. Among them, point of interest(POI) recommendation is a research hotspot in the current context. As a kind of graph data, social network can naturally express the data structure in real life. In view of the current POIs recommendation research ignoring the diversity of graph data, we proposed a POI recommendation based graph convolutional neural network (PBGCN) model, which used the check-in information, popularity characteristics of interest points, and users’ social behaviors to recommend interest points through graph convolutional neural networks(GCN). Compared with other latest recommendation methods, our model has improved accuracy. This proves the feasibility of GCN in point of interest recommendation.
Synthesis, Characterization and Optoelectronic device application of ZnO nano structure

A clear ZnO nanowire was obtained using the Pulse laser deposition technique on a silicon and quartz substrate. Thermal oxidation at 650 °C for 1 hour was used, employing conventional tube furnace. The structural properties ensure the formation of quantizing ZnO. The electrical properties of prepared ZnO NWs/Si films reveal a good rectification with maximum optical Responsivity in the UV region reach to 0.3A W.
Retraction Retraction : Securing Connected & Autonomous Vehicles : Challenges posed by Adversarial Machine Learning and the way forward

Associated and self-ruling automobiles (CAVs) will shape a foundation for the future cutting edge canny transport frameworks (ITS) giving comfortable travelling, street wellbeing, alongside various worth added administrations. Such a change—which would be fuelled by attendant advances on advances for AI (Machine Learning) and remote interchanges—will empower a future vehicular biological system that is better included and more effective. Notwithstanding and are sneaking security issues identified with the utilization of Machine Learning in a particularly basic setting where a mistaken Machine Learning choice may not exclusively be an aggravation yet can prompt loss of valuable lives. Here it’s present a top to bottom outline of the different difficulties related with the utilization of Machine Learning In vehicular organizations. Moreover, we form the Machine Learning Pipeline of CAVs and present different potential security issues related with the selection of Machine Learning Strategies. Specifically, it’s center around the point of view of antagonistic Machine Learning Assaults on CAVs and framework an answer for protecting against ill-disposed assaults in different settings.
Measurement of uranium concentrations in urine samples of adult healthy groups in Najaf governorate with estimation of urine concentrations of 8-OHdG compound as biomarker for DNA damage

In this study, 88 healthy adults in Al-Najaf governorate of Iraq have been divided into three groups according to the type of exposure to radiation including environmentally uranium exposures n=29 (G1), occupationally exposures n=27 (G2), and non-uranium exposures n=32 (G3). All groups were subjected for estimation of uranium concentration in urine samples using CR-39 SSNTD method beside measurement of urine concentrations of 8-OHdG compound as a biomarker for oxidative DNA damage using Elisa technique. Reactive oxygen species produced under the effect of many causes including ionizing radiation exposure, (0 H)free radical induced the formation of 8-OHdG compound from cellular and mitochondrial DNA through attack of C-8 in purine ring of guanine base. The results showed that mean values of uranium concentration were (1.836±0.426 μg/L), (2.02±0.404 μg/L) and (1.755± 0.437 μg/L) for group1, group2 and group3, respectively. While mean values for 8-OHdG were (49.810±15.484 ng/ml), (47.717±14.232ng/ml) and (46.769±14.249ng/ml) for the three groups, respectively. No significant statistical differences were recorded between all data obtained. These results suggested the presence of uranium pollution in all groups including the non-exposed group as compared with reference value of (WHO, 2004). Results also demonstrate the presence of DNA oxidative damage in all groups according to concentration levels of 8-OHdG in urine samples. Therefore, the uranium pollution in urine samples of the examined groups indicate the presence of chronic low dose radiation source in Najaf governorate, which might be the cause of DNA damage as reflected by the urinary levels of 8-OHdG compound.
CT reconstruction algorithm based on truncated TV

This paper proposes a simultaneous algebraic reconstruction technique (SART) based on truncated total variation (truncated TV), called SART-truncated TV algorithm (SART-TTV). Truncated TV is particularly effective in removing unimportant details while preserving significant edges. Moreover, the truncated TV only penalizes part of the gradient of the image, so it can solve the problem of excessive smoothness caused by the classic TV penalizing larger gradient amplitude. In this paper, the classic Shepp-Logan model is simulated and the results of sparse angle reconstruction are compared and analyzed. The results show that the quality of the reconstructed image obtained by the SART-TTV algorithm is the highest.
Neural Network Quantization Methods For Voice Wake Up Network

Quantifying the pre-trained neural network model can reduce its storage size and speed up the forward-inference process. Previous researches for the quantization of voice wake up networks couldn’t reach our anticipation for applying it to applications. The voice wake up network is mainly composed of recurrent neural network and classification function. Recurrent neural network can take preorder words into consideration and it widely used in voice network. In this paper, we propose method to quantize the structure of recurrent neural networ including RNN, GRU and LSTM by optimizing the method of quantifying the activation function. At the same time, using the translation invariance of the classification function to improve classification accuracy on Speech commands dataset version 2. Using the method proposed in this paper, we can make the quantized model achieve a better voice wake up rate.
Photophysical and morphological properties of spin-coated and self-assembled carbazole derivative thin films

In this work, single-layered, bi-layered and tri-layered poly(vinyl-carbazole) (PVK) thin films were produced by spin-coating and self-assembling techniques and their morphological and photophysical properties were determined by time-resolved fluorescence spectroscopy, atomic force microscopy (AFM) and fluorescence microscopy. The effect of distinct aggregates formed in thin films on their photophysical and morphological properties was determined, evidencing that procedure control is essential to determine PVK thin film application. It was found that sandwich excimers are dominant in self-assembled films, evidenced by longer fluorescence lifetimes and red-shifted emissions, while in spin-coated films, shorter fluorescence lifetimes evidenced more efficient non-radiative energy transfer. AFM showed that consecutive deposited layers result in more uniform, thinner films when produced by spin-coating, but showing aggregation heterogeneity as evidenced by fluorescence spectroscopy. Yet, self-assembled films are rough, heterogeneous and thicker, showing prevalence of less variety of aggregates. These are crucial findings to address PVK application.
Design, modeling and experimental validation of a micro cantilever beam with an electro-controllable twisting ability

The structure design of micro cantilever beams(MCBs), which has a significant effect on MCBs’ controllable actuation range, is usually restricted by the establishment of the inner electric circuit. This paper proposes the design of a micro electro-controllable twisting cantilever beam (METCB). The architecture and the fabrication diagram of the proposed METCB are presented before its features are theoretically and experimentally validated. In detail, the analytical validation includes a sequence of two models: electro-thermal and thermo-mechanical. Through these two models, the thermal distribution along the METCB and the deformation brought by the thermal distribution are investigated, respectively. On the basis of the design and equations, the METCB samples have been manufactured using a planar deposition technique with an e-beam evaporator. Two in-site experiments are then conducted to sequentially figure out the upper voltage limit of the inner electric circuit and the maximum temperature limit of the METCB. Finally, another experiment is carried out to explore the relation between the applied voltage and the relative twisting angle of the METCB sample. The theoretical prediction and the observed experiment result both prove the feasibility of the proposed METCB and its electro-controllable twisting ability.
Classification and Evaluation for Microblog Popularity Prediction

In recent years, with the rapid development of the Internet, especially the mobile Internet, social networks have entered the stage of vigorous development and become one of the main sources of information. User-generated contents (UGC) on social platforms can spread information along social networks at an astonishing speed. Existing literature has proposed many prediction methods for the popularity prediction on social networks. This paper presents a classification and establishes a unified evaluation framework of popularity prediction methods for microblogs. More specifically, we divide these mainstream prediction methods into four types: feature based methods, time series methods, collaborative filtering methods and deep learning methods and conduct experiments on the real-world weibo data using these methods to predict. Finally, according to four indicators, including accuracy, efficiency, robustness and bias, we evaluate and compare the methods. Based on the prediction and evaluation results, this paper summarizes and draws the following research conclusions:(1) The deep learning method has the characteristics of high accuracy, high robustness and low bias. The DeepFM method, one of the deep learning methods, performs better than the other three prediction methods when using temporal data as its input. (2) The feature based methods only using temporal features are basically consistent with those using all available features, indicating that the temporal feature has strong prediction power. Therefore, the ‘peeking’ strategy that monitors the early response of users in the initial period after the items are posted is effective. Additionally, the predictive power of temporary features can be further amplified in time series methods and deep learning methods. (3) Due to the sparse user-item interaction in social networks, the accuracy and efficiency of collaborative filtering methods are low, which makes it impossible to predict the popularity of items in social networks well.
The existing technology and the application of digital artificial intelligent in the wastewater treatment area: A review paper

Wastewater treatment using various existing technologies, including advanced oxidation processes, adsorption, and membrane separation for various pollutants removal from industrial and municipal wastewater streams, is an essential aspect of reaching environmental sustainability to keep human well-being and healthy economic growth. However, some challenging elements along with the wastewater treatment process affect pollutant removal efficiency and other resources. This condition will lead to various uncertainty in the wastewater treatment system related to the fluctuations in the quality of treated water and wastewater, operation costs, and environmental risk. Artificial intelligence can then be such a reliable solution to predict and minimize those complications and optimize the process and parameters implemented in the wastewater treatment system.
Intelligent Library Architecture Based on Edge Computing

The development of the Internet of Things promotes the integration of physical space and information space. In addition, more and more people, machines and things are connected to the information space. The centralized cloud computing model has been gradually hard to accommodate the resulting massive data treating requirements. However, the emerging edge computing provides a new technical means to solve this problem. By comparing and analyzing the basic framework and features of edge computing and cloud computing, taking the typical case of Intelligent Library as an example, this paper constructs an Intelligent library system architecture based on edge computing. It analyzes the functions of each level of the architecture. The architecture can reduce the minimum delay and network bandwidth pressure of Library intelligent service, thus saving the data processing cost of Intelligent Library.
Does statistical model perform at par with computationally expensive general circulation model for decadal prediction?

Decadal predictions have gained immense importance over the last decade because of their use in near-term adaption planning. Computationally expensive coupled model intercomparison project phase 5 general circulation models (GCMs) are initialized every 5 years and they generate the decadal hindcasts with moderate skill. Here we test the hypothesis that computationally inexpensive data-driven models, such as multi-variate singular spectrum analysis (MSSA), which takes care of trends and oscillations, performs similar to GCMs. We pick up one of the most complex variables having low predictability, Indian summer monsoon rainfall (ISMR) and its possible causal sea surface temperatures (SST). We find that the MSSA approach performs similar to the GCMs in simulating SSTs beyond their nonlinear limits of predictability, which is ∼12 months. These SSTs are used for decadal predictions of ISMR and show improved skills compared to the GCMs. We conclude that data-driven models are possible alternatives to computationally expensive GCMs for decadal predictions.
Review of Trajectory Planning for Industrial Robots

More and more industrial robots need high-precision trajectory control, but the traditional PID control is difficult to achieve. The simulation diagram and pose of the collision free mechanism are drawn by using rosty software. At the same time, through the experiment of MOTOMAN-HP3 robot, using VC + + application program to call the external interface function of motocom32 software, the application program planning is realized. The linear and circular trajectory planning of industrial robot in Cartesian space is studied. Linear interpolation algorithm based on spatial parabola transformation and circular interpolation algorithm based on local coordinates are proposed. The algorithm is applied to a self-designed 5-DOF spraying robot. An evolutionary algorithm based optimal trajectory planning method for PUMA560 robot is proposed. The method is based on the elite non dominated sorting genetic algorithm (NSGA-II). The results show that the minimum angular displacement, angular velocity and angular acceleration of X3 parallel robot are 56, and the maximum angular displacement, angular velocity and angular acceleration are 89.
In Situ Self-Assembled FeWO4/Graphene Mesoporous Composites for Li-Ion and Na-Ion Batteries

With the growing demands for large-scale applications, rechargeable batteries with cost-effective and environmental-friendly characteristics have gained much attention in recent years. However, some practical challenges still exist in getting ideal electrode materials. In this work, three-dimensional FeWO4/graphene mesoporous composites with incredibly tiny nanospheres of 5–15 nm in diameter have been synthesized by an in situ self-assembled hydrothermal route. First-principles density functional theory has been used to theoretically investigate the crystal structure change and the insertion/extraction mechanism of Li and Na ions. Unlike most graphene-coated materials, which suffer the restacking of graphene layers and experience significant irreversible capacity losses during charge and discharge process, the as-prepared composites have alleviated this issue by incorporating tiny solid nanospheres into the graphene layers to reduce the restacking degree. High capacity and excellent cyclic stability have .
Convolutional neural networks for solving problems of signal processing from segmental distributed fiber optic measuring networks

The work is devoted to the problem of critical situations recognition in real-time and localization of increased acoustic vibration in critical objects, based on the signals from segmental distributed fiber-optic measuring networks (DFOMN). To solve the problem, an architecture is proposed, and a model of a situational approach to using the Convolutional Neural Network as an effective classification method for processing large data arrays of the DFOMN is developed.
Synthesis of Modified Graphite with High Crystalline Na-LTO by Simple Doping Method.

Graphite doped with Na-Li4Ti5O12 (Na-LTO) for lithium-ion batteries with stable rateability were prepared in a short time by simple grounding which followed with the short calcination time. High crystalline Na-LTO improved the diffusivity of the Li-ion kinetics and stability. Therefore, Graphite/Na-LTO (G-NaLTO) was compared with the pure graphite to find out the differences in terms of XRD pattern and electrochemical performance. XRD analysis showed there was no significant differences pattern from G-NaLTO with commercial graphite. In terms of electrochemical performance, G-NaLTO which was doped by 1 wt% Na-LTO, presented a high initial charge capacity 356.69 mAh g−1 at 0.1C and stable discharge rate-capability at 0.2C and 1C were 264.64 mAh g−1 and 220.65 mAh g−1, respectively, after 15 cycles. G-NaLTO with a composition of 3 wt% Na-LTO shows a decreasing electrochemical performance so it can be concluded that the addition of 1 wt% LTO by the doping method is the most optimal.