Miracle-dz/UniFAR-FTUs huggingface dataset:
A Nephogram Recognition Algorithm Based on Cloud Computing Platform

Using image processing technology, a k-means algorithm based selective neural network ensemble method and Hadoop processing technology, combine cloud platform with BP neural network algorithm, K-means algorithm and AdaBoost algorithm, to study cloud shape recognition technology based on cloud computing platform. The research results show that the recognition precision of image recognition technology of cloud computing platform is higher than that of cloud shape recognition technology based on traditional BP AdaBoost ensemble algorithm and single operation image recognition technology based on a k-means algorithm based selective neural network ensemble method.
A direct inverse hysteresis model and its application in reluctance actuators.

This article investigates the inverse hysteresis model for the hysteresis compensation. A direct inverse(DI) model based on the new inverse operators is proposed to model the inversion of hysteresis and compensate for the hysteresis effect. The extended application of the DI model for rate-dependent hysteresis in the reluctance actuator is presented as well. Some simulations and experiments are executed to validate the proposed DI hysteresis model. Results show that DI has high accuracy in fitting the inverse hysteresis loops, and can alleviate hysteresis nonlinearity significantly as a feedforward compensator for both the rate-independent and rate-dependent hysteresis.
Satellite mission planning for moving targets observation via data driven approach

Satellite mission planning is an important premise for earth observation. Traditional satellite mission planning is mainly aimed at fixed ground targets, which cannot meet the increasingly complex mission requirements. This paper considers the moving target observation, and puts forward a method of satellite mission planning via data driven approach. This method forecasts the future track and position information of the moving target through a modified Long Short-Term Memory (M-LSTM) networks algorithm, and proposes a Constraint Satisfaction Genetic Algorithm (CSGA) to plan the missions of moving target observation based on the results of M-LSTM algorithm. In view of the complexity of the constraint and task conflict in the moving target observation, CSGA embeds the constraints into the genetic algorithm through conditional forms, and a conflict resolution operator is designed in CSGA to resolve task conflicts. Simulation results demonstrate the efficiency of the LSTM-CSGA method to solve the mission planning and get a higher observation accuracy.
Disturbance Observer-Based Attitude Control for Hypersonic Vehicles with Input Constraint

In this paper, a robust attitude control strategy for hypersonic vehicles is proposed, which combines a disturbance observer and an anti-saturation method with the basic feedback linearization controller. First, the employed nominal model of hypersonic vehicles is transformed through feedback linearization method and stabilized by a basic controller. Then, a disturbance observer is proposed to generate the estimate of aerodynamic parameter uncertainties and complex disturbances on the vehicle model. Furthermore, an auxiliary system is established to reduce the impact of input constraints. The designed control method presents good attitude tracking ability and robustness. Finally, the effectiveness of the proposed technique is illustrated by several simulations.
Kajian SDM kesehatan di Indonesia

Kami dedikasikan buku ini bagi para tenaga kesehatan yang berjuang di garis depan pelayanan, akademisi dan peneliti di bidang kesehatan, praktisi SDM Kesehatan serta mahasiswa yang mendalami rumpun ilmu kesehatan. Buku ini terdiri dari 6 bab meliputi (1) Sumber Daya Manusia Kesehatan, (2) manajemen Sumber Daya Manusia Kesehatan, (3) Tantangan SDM Kesehatan, (4) Tool SDM Kesehatan, (5) Studi Kasus SDM Kesehatan dan (6) SDM Kesehatan Global.
Accurate and Efficient Dynamic Simulations of Ferroelectric Based Electron Devices

In recent years electron devices based on ferroelectric materials have attracted a lot of interest well beyond FeRAM memories. Negative capacitance transistors (NC-FETs) have been investigated as steep slope transistors [1], [2], and Ferroelectric FETs (Fe-FETs) are under intense scrutiny also as synaptic devices for neuromorphc computing, where the minor loops in ferroelectrics can allow to achieve multiple values of conductance in read mode [3], [4], [5]. Furthermore, the persistence of ferroelectricity in ultra-thin ferroelectric layers paved the way to ferroelectric tunnelling junctions [6], where a polarization dependent tunneling current can be exploited to realize high impedance memristors, amenable for ultra power-efficient and thus massive parallel computation.
Multiple endocrine neoplasia type 2A

: An adult woman 43 years old, with Multiple Endocrine Neoplasia type 2A (MEN2A). Clinical diagnosis of MEN2A was made based on the chief complaints of abdominal pain and a lump in the front left side area of neck accompanied with weight loss, from laboratory examination revealed an increased level of plasma levels of calcitonin levels with the result of 6359 pg/ml and 24 hours metanephrine urine levels was 534 µ g/24 hours. On the results of thyroid ultrasound examination showed nodules with malignant characteristics, multiple parathyroid gland hyperplasia and the discovery of a solid mass in the left adrenal gland area by an abdominal CT Scan with and without contrast. MEN2A case is considered as one of the few endocrine malignancies with a rare incidence especially in Indonesia.
LMI-based Design of Robust Static Output Feedback Controller for Uncertain Linear Continuous Systems

This paper proposes the static output feedback (SOF) controller design method for uncertain linear continuous-time systems based on the Linear Matrix Inequalities (LMIs) technique. In the present work, we consider norm-bounded parametric uncertainties. We show that the existence of the SOF for the closed-loop uncertain linear systems is cast as the feasibility of a set of Bilinear Matrix Inequalities (BMIs). Using some technical lemmas, the BMIs are transformed into LMIs, which are numerically exploitable. Thus, we present new LMI stability conditions for the SOF design using two different approaches. We show that our new LMI conditions are less restrictive compared with those available in the literature. A numerical example is illustrated in the end to show the superiority of our design method.
State Estimate and Control for Multi-rotors UAV: Theory and Experimentation

In this paper, based on the measurements of Inertial Measurement Unit (IMU) and Visual Odometry (VIO) system, the state estimate and control algorithms for multi-rotors UAV are investigated. Firstly, we developed the kinematic model of acceleration and angular velocity. Then we designed an unscented Kalman filter to achieve full state estimation, including position, velocity and attitude. Our method is able to provide a high rate and smooth UAV’s states to the controller. Based on the UAV’s motion model, a continuous controller is presented. Finally, experimental results on hovering flight of Multi-rotors UAV in GPS-denied environment show the good performance of our algorithms.
Rolling Bearings Fault Diagnosis via 1D Convolution Networks

This paper proposes a convolution neural network based fault diagnosis for rolling bearings. Analysis about why convolution networks fit for vibration signals is first introduced. Then, an end to end network that reflects the raw vibration signals to fault types is built. Hyper parameters are tuned guided by the similarities and differences between images and vibration signals. Some conclusions about hyper parameters are drawn from hyper parameters tuning process, which helps us get a deeper understanding of convolution network’s application to vibration signals. The experiment is taken using Case Western Reserve University bearings dataset and 101 categories including different motor speeds are divided from the raw data. The result shows that our model achieves about 99% accuracy. What’s more, even the frequency differences among motor speeds are very small (about 20 RPM, less than 0.5 Hz), our model can also classify them with high accuracy.
Visualization of Fibroid in Laparoscopy Videos using Ultrasound Image Segmentation and Augmented Reality

Though they rarely become malignant, the surgical removal of fibroids (uterine myomas) is commonly considered to prevent any possible future risks. As the least invasive intervention, endoscopic surgery is the most popular approach for this aim. However, since these compact tumors reside in the deep (muscle/connective) tissues of the uterus, they are hardly visible using only the video stream provided by the endoscopic camera. Thus, conform to the current general trend in human surgery, in this paper we propose a multimodal approach to make these tumors more visible during endoscopic interventions, namely, the reconstruction of the whole three-dimensional model of the uterus from ultrasound images and segmentation of the fibroids using this modality. Then, we map the result of the segmentation on the surface of the uterus, hence they become visible during endoscopic surgery. Similar efforts have already been made, considering the usage of MRI for this purpose, but ultrasound image acquisition is more widely available, faster, and cheaper next to the lower image quality. Our aim is to use the output of the 3D ultrasound imaging device during the laparoscopic surgery. Our segmentation pipeline processes the ultrasound images and consists of Otsu's thresholding using a special mask derived from image averages and morphological snakes to extract uterus boundary. As the final step, we project the segmented 3D model of the uterus with its lesion on an endoscope camera flow in real time to provide an augmented reality application.
Sinusoidal Modulation of FM Accelerometers with Integrated Oscillator and Frequency Digitization

A new operational mode of a frequency-modulated (FM) accelerometer for simultaneously improved full-scale, stability and bandwidth is presented. The system relies on a continuous sinusoidal modulation of the electromechanical configuration of a single resonator, which gradually reverses the sign of its frequency sensing: as a consequence, a secondary amplitude-modulation (AM) is superimposed to the primary FM effect. The following demodulations automatically cancel temperature-related drifts of the resonator frequency. The accelerometer is coupled to an integrated circuit that embeds an analog oscillator and a frequency digitization stage, with an overall consumption of $148\ \mu\mathrm{A}$. A sub-80-µg/K drift coefficient and, at the same time, a sub-0.25% linearity error over 42 g of input acceleration are demonstrated. The achievable bandwidth is half of the chosen AM modulation frequency.
Algorithm for Enhanced Design Set of all Possible Unipolar Orthogonal Codes

Orthogonal property of signal helps in sending multiple data without any error. In Optical Fiber Communication, the fiber provides a very high bandwidth which can be improved further with finding more number of Orthogonal codes. This Paper discusses the possible combinations to find maximum set of Orthogonal Codes with the parameters, Code Length (n), Weight of Code (w) or number of Ones (w), Auto-correlation (λa) and Cross-correlation (λc) Constraints. Further, the paper also illustrates the number of Maximum Orthogonal Code Sets possible.
Analog beamforming for Full-Duplex Millimeter Wave Communication

Full-duplex (FD) communication has the potential for significant improvements in spectral efficiency, as long as the problem of self-interference (SI) can be overcome. For millimeter wave (mmWave) systems with large antenna arrays, beamforming based SI mitigation is attractive because of the large number of degrees of freedom available. Previously proposed precoder- combiner designs, however, suffer from a large performance loss under the hardware-constrained architectures required for operation at mmWave. In this context, we develop a new algorithm for the design of constant-amplitude analog precoders and combiners of an FD mmWave single-stream bidirectional link, significantly reducing the aforementioned performance loss.
A Mirrored Motion Remapping Method in Telemanipulation-based Face-to-Face Dual-arm Robot Teaching

With the increasing demands from industrial and daily usage, dexterous robot manipulation in unstructured environment becomes a very important technology to make robots behave smartly and interact with people intelligently. To deal with the challenge in efficient task teaching of a robot with multiple Degree-of-freedom (DoF) for dexterous manipulation, this paper develops a mirrored motion remapping method, for enabling a human operator to teach a dual-arm robot face-to-face by direct human motion through handheld HTC VIVE controllers. The provided implementation results shows that the mirror mapping based telemanipulation enables an operator to visualize the on-site conditions and understand the robot actions nicely while telemanipulating a dual-arm robot. The robot is guided to complete variation of complex tasks which involves dexterous dual-arm manipulation efficiently, based on the human-in-the-loop telemanipulation. Such method is very useful to support face-to-face teaching on dual-arm robots and enhances capabilities of dual-arm robots on more dexterous manipulation skills in the near future.
Low-Overhead Remote User Authentication Protocol for IoT Based on a Fuzzy Extractor and Feature Extraction

The Internet of things (IoT) is a system of smart technologies and services that mutually communicate data between users and devices or between devices via the Internet. Since data are shared between a remote user and various sensing devices over a network, it is essential to design a secure, lightweight and efficient remote user authentication protocol for the IoT environment. In the context of security and network privacy, mutual authentication is necessary for securely accessing the services of the IoT environment. However, the IoT faces substantial new challenges realizing mutual authentication due to IoT devices constraints. In this paper, we present a lightweight, robust and secure authentication protocol that satisfies constraints on IoT devices. The proposed protocol is based on level 3 feature extraction, fuzzy extraction of the user’s biometrics, one-way hash functions and XOR operations and includes (1) three-factor authentication (user password, biometrics and smart devices), (2) mutual authentication, (3) a session key, and (4) key freshness. Furthermore, we have used the Burrows–Abadi–Needham logic to prove the authentication of our proposed protocol. In addition, our proposed protocol does not require additional hardware or a resource-constrained cryptosystem, and for that reason; hence, it has the lowest computational cost on the IoT nodes (0.003_ms), the lowest total computational cost (0.071_ms), and the lowest communication cost (2784 bits) compared with other relevant works. Moreover, we have conducted an informal security analysis to prove its ability to withstand well-known malicious attacks, such as replay attacks, impersonation attacks, password change attacks, man-in-the-middle (MITM) attacks, and denial of service (DOS) attacks.
Object-aware Semantic Mapping of Indoor Scenes using Octomap

In order to enable robots to provide high-level services in complex indoor environments, it is necessary to improve the robots’ ability to cognize the environments. Most of the existing research is focused on indoor 3D reconstruction and semantic segmentation without the organization and maintenance of object recognition results. In this paper, we present an approach to build a 3D semantic map that includes both voxel-based geometrical demonstrations and object-aware entities with the combination of Simultaneous Localization and Mapping (SLAM) and Mask Region-based Convolutional Network (Mask R-CNN). An extended seeded region growing algorithm is designed for 3D segmentation refinement, and an octree-based framework octomap is used to present 3D map in replacement of point cloud map. We present experiments in a simulated home environment and the experimental results verify the accuracy and efficiency of our method.
On Computer-Aided Diagnosis of Prostate Cancer from MRI using Machine Intelligence Techniques

Prostate cancer is one of the major cause of cancer death among men. Mortality rate due to prostate cancer can be reduced significantly by early diagnosis and treatment planning. Magnetic Resonance Imaging (MRI) is a major modality for early detection of prostate cancer. Recent studies have proven that MRI can be used for grading of prostate cancer also. In this paper, a survey on computer-aided detection and grading of prostate cancer is done exploring the state-of-the-art techniques. The paper examines various stages of computer-aided diagnosis of prostate cancer including detection of cancer, estimation of clinical significance of cancer and grading of cancer. The survey compares the performance of different techniques available in the lliteraure and provides directions for further research.
Behavior of a Hippocampal Spiking Network and FPGA Implementation

The hippocampus in human brain is responsible for memory processing and tasks learning, which has long been one of the main interests of many researchers. In this paper, a spiking neural network of the hippocampus is realized based on a designed task. In the task, the model rat firstly gets familiar with the environment and finds item in one of the pots and finally gets a reward for making the correct response. Through the network, the feature of the hippocampal neurons can be simulated in software successfully. The three-layer network was built based on the spike-timing dependent synaptic plasticity and the synaptic weights will be modified between layers during task which can finally achieve the memory-related behavior of the model rat. Besides software realization, we also utilize Field Programmable Gate Array (FPGA) to reproduce the characteristics of the network in real time. The results show that the spiking neural network of the hippocampus can mimic the memory-related behavior of the model rat.
A Compact Design of Circularly Polarized Fractal Patch Antenna for 5G Application

A compact size of ring loaded fractal patch antenna is proposed. The proposed antenna is designed over the circular shape of the substrate material. The substrate material used in design process is Rogers RT/Duroid 5880 having relative permittivity (εr) of 2.2 and the radius of the substrate material is 10 mm. A fractal shape square patch loaded width four rings at each corner is feed by the co-axial probe feeding. The fractal shape of antenna improves the bandwidth and the ring helps in impedance matching also improves the gain of the antenna. The proposed antenna works on resonant frequency of 24 GHz. The measured bandwidth of the proposed antenna is 5GHz with band ranges from 20 GHz to 25GHz. The measured gain of the proposed antenna is 6.5 dB. The proposed antenna is circularly polarized in nature and it is suitable for 5G application.
Bearing Fault Diagnosis Based on One-Dimensional Convolution Network and Residual Training

Bearings are an important part of large industrial production equipment, and their damage is often accompanied by significant economic losses and personal safety issues. Algorithms based on the traditional machine learning, the recognition rate is not high, and the data preprocessing has great influence on the result. Models based on deep learning, which is relatively simple, and the shallow features extracted include noise. Aiming at the above problems, this paper proposes an end-to-end deep one-dimensional convolution residual network (DOD-CRN) fault diagnosis model. Training on the original vibration signal, and the feature extractor is constructed according to the characteristics of the data, which included the extraction of shallow features and high-level features. Experiments show that DOD-CRN has 98.3% fault recognition rate under complex fault signals, which is higher than other algorithms and can effectively detect faults.
Violência contra as mulheres por parceiros íntimos: usos de serviços de saúde

OBJETIVO: Estimar a associacao entre violencia por parceiro intimo (VPI) e uso de servicos de atencao primaria a saude em Sao Paulo. METODOS: Estudo transversal com selecao dos servicos por amostragem de conveniencia e de mulheres usuarias desses servicos por amostragem do tipo consecutivo. As unidades amostrais finais de 2.674 mulheres de 15 a 49 anos de idade foram categorizadas, segundo a ocorrencia e repeticao de episodios de qualquer tipo de VPI na vida, como "nao", "sim com alguma repeticao" e "sim com muita repeticao". Por meio de regressao logistica polinomial, testou-se a associacao entre VPI, uso de servicos de saude e diagnosticos ou queixas das mulheres usuarias (tipo e frequencia de registro), ajustadas pelas variaveis sociodemograficas e de saude sexual e reprodutiva. RESULTADOS: Foi observada uma prevalencia de 59% de VPI independente de sua repeticao. O maior numero de consultas mostrou-se associado com VPI repetitiva, apos o ajuste dos efeitos de possiveis variaveis de confundimento. Os diagnosticos e/ou queixas de agravos psicoemocionais registrados, mais de uma vez, no ultimo ano, mostraram-se associados com VPI, aumentando sua magnitude com a maior repeticao da violencia. CONCLUSOES: E crucial um maior diagnostico dos casos de VPI entre mulheres usuarias dos servicos de saude, bem como a implementacao de acoes que previnam a violencia e de cuidado relativamente as necessidades particulares de saude dessas mulheres. Tais medidas, se adotadas, produzirao impactos tambem no padrao de uso dos servicos.
Whispering Gallery Microlaser Fabrication using Er3+ Doped Silica Based on Dip Coating

Silica whispering gallery mode microlasers have potential applications in sensing and communications. One method is to fabricate the microcavity through originally doped glasses with gain materials such as erbium (Er3+ ions) [1]. Besides, rare-earth ions and quantum dots can be doped into the silica microcavity through ion implantation method based on a complicated CMOS process [2] or the low-cost sol-gel processing method [3]. The sol-gel method is more conventional, however, still requires a relatively complicated process, also cracks and defects may emerge during the process, which seriously degenerates the Q factors of the microcavities [4].