Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Performance of Power Allocation Schemes in Ultra Dense Networks

In this paper, user association and power allocation in the uplink transmission of ultra-dense networks are studied. Solutions are proposed for cell grouping and resource sharing based on cell locations. The performance of non-orthogonal multiple access (NOMA) is compared to other power allocation schemes used in literature, namely, power control, the waterfilling algorithm as well as uniform power allocation. It is shown that while NOMA guarantees fairness among transmitting nodes, other power allocation schemes achieve higher aggregate throughput at the expense of some nodes deprived their right for transmission. Therefore, in data-centric application, e.g. Internet of Things-based application and Machine Type Communication-based application, where higher data rate transmission is prioritized irrespective the transmitting nodes, NOMA cannot be recommended as an optimum solution.
Classification vs Regression by CNN for Handwashing Skills Evaluations in Nursing Education

Nosocomial infections have been of interest to experts in the medical field due to their ability to put human life at risk. Thus, nosocomial infection control is extremely important and methods vary. Handwashing methods can be applied to medical practice where there is a need for handwashing skills. Many studies have proposed handwashing skills evaluation methods. Today, quantitative evaluation methods make it possible to evaluate handwashing skills for the entire hand. However, these methods cannot provide numerical feedback on which part of the hand is left unwashed. Additionally, it is unclear whether handwashing skills are maintained after handwashing training is finished. In this paper, we propose a new quantitative evaluation method based on deep learning and investigate whether handwashing skills continue to take root after education is complete. Our experimental subjects were one hundred sixty-four students in the School of Nursing at the University of Toyama who were handwashing skills beginners. Collected image data were divided into training data and test data and a convolutional neural network (CNN) was trained and validated. The results show the CNN F-value was 93.0%. Next, we collected data from 23 students from the School of Nursing who had finished handwashing lectures and 16 handwashing beginners. Experienced handwashers demonstrated almost the same skills as those of beginners.
Reinforcement Learning Control of a Shape Memory Alloy-based Bionic Robotic Hand

Shape Memory Alloy (SMA) with the advantage of high power-to-weight ratio as a promising alternative to traditional actuators is widely applied to robotic systems. Due to the merits of low noise, driving voltages and small size SMA is used in robotic hand. Hysteresis phenomenon of SMA and dynamic model of the SMA-based robotic hand are complexities in control system. In this paper, we apply a reinforcement learning (RL) algorithm to the robotic hand actuated by SMA for motion control. The proposed hand can achieve the desired bending state and grasp the object with desired bending state effectively and steadily.
Micro Motion Amplification–A Review

Many motion-active materials have recently emerged, with new methods of integration into actuator components and systems-on-chip. Along with established microprocessors, interconnectivity capabilities and emerging powering methods, they offer a unique opportunity for the development of interactive millimeter and micrometer scale systems with combined sensing and actuating capabilities. The amplification of nanoscale material motion to a functional range is a key requirement for motion interaction and practical applications, including medical micro-robotics, micro-vehicles and micro-motion energy harvesting. Motion amplification concepts include various types of leverage, flextensional mechanisms, unimorphs, micro-walking /micro-motor systems, and structural resonance. A review of the research state-of-art and product availability shows that the available mechanisms offer a motion gain in the range of 10. The limiting factor is the aspect ratio of the moving structure that is achievable in the microscale. Flexures offer high gains because they allow the application of input displacement in the close vicinity of an effective pivotal point. They also involve simple and monolithic fabrication methods allowing combination of multiple amplification stages. Currently, commercially available motion amplifiers can provide strokes as high as 2% of their size. The combination of high-force piezoelectric stacks or unimorph beams with compliant structure optimization methods is expected to make available a new class of high-performance motion translators for microsystems.
Parallelly Running k-Nearest Neighbor Classification Over Semantically Secure Encrypted Data in Outsourced Environments

Cloud services with powerful resources are popularly used to manage exponentially increasing data and to carry out data mining to analyze the data. However, a data mining involving query can cause privacy problems by disclosing both the data and the query. One task in data mining, classification, is used in a wide range of applications, and we focus on <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN) in this study to realize classification. Although several studies have already attempted to address the privacy problems associated with <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN computation in a cloud environment, the results of these studies are still inefficient. In this paper, we propose a very efficient and privacy-preserving <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN classification (<inline-formula> <tex-math notation="LaTeX">$\text{P}k$ </tex-math></inline-formula>NC) over encrypted data. While the amount of computation (encryptions/decryptions and exponentiations) and communication of the most efficient <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN classification proposed in prior studies is bounded by <inline-formula> <tex-math notation="LaTeX">$O(kln)$ </tex-math></inline-formula>, that of the proposed <inline-formula> <tex-math notation="LaTeX">$\text{P}k$ </tex-math></inline-formula>NC is bounded by <inline-formula> <tex-math notation="LaTeX">$O(ln)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> is the domain size of data and <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of data. When conducting experiments with the same dataset, the prior <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN classification took 12.02 to 55.5 minutes but <inline-formula> <tex-math notation="LaTeX">$\text{P}k$ </tex-math></inline-formula>NC took 4.16 minutes. Furthermore, since <inline-formula> <tex-math notation="LaTeX">$\text{P}k$ </tex-math></inline-formula>NC allows to be carried out in parallel for each data, its performance can be improved extremely if it is carried out on machine to allow more numerous threads. <inline-formula> <tex-math notation="LaTeX">$\text{P}k$ </tex-math></inline-formula>NC protects the privacy of dataset, input query including the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN result, and does not disclose any data access patterns. We propose several protocols to serve as building blocks to construct <inline-formula> <tex-math notation="LaTeX">$\text{P}k$ </tex-math></inline-formula>NC and formally prove their security. In particular, we propose efficient protocols that privately find <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> largest or smallest elements in array.
Reservoir computing with inertial manifold*

We propose a spatiotemporally continuous framework of reservoir computing that has an inertial manifold. Using the theoretical results concerning the infinite-dimensional dynamical systems, we first propose a new model that guarantees the existence of a strong solution. Then, we prove that the proposed model actually has an inertial manifold, which is easier to deal with in practical situations. Furthermore, we also prove the following: given two input signals tending to different stationary states as time infinitely elapses, the corresponding stationary state of the reservoirs tend to different states.
The autonomous control of the Service-Oriented robot arms based on the artificial intelligence object identifying technology

With the rapid evolution of Artificial Intelligence (A.I.) technology, traditional image recognition approaches are replaced, because the speed of the detection is too slow and not accurate, especially in robot fields. The sensing abilities are the most important feature with a service robot. The object recognition is one of the sensing abilities, in order to let the service robot can detect the object extremely fast and accurate, the robot use Artificial Intelligence technology, the sensing ability of a robot could be improved significantly. In this study, YOLO object Identification approach is used to enhance the robot sensing to locate a particular object, and then to pick it up. In order to reach this goal, the CNN and deep learning technologies are studied.
Voltage Gain Extension Techniques for High Step-Up Galvanically Isolated DC-DC Converters

This paper discusses voltage gain extension intended for the galvanically isolated impedance-source DC-DC converters under the principle of combined energy transfer when the energy is transferred from the primary to the secondary side using an isolation transformer and a coupled inductor of the impedance-source network. Different realization approaches of the combined energy transfer are evaluated; the main emphasis is on the power conversion efficiency and realization complexity. Also, the application of topology morphing control to the galvanically isolated impedance-source DC-DC converters with combined energy transfer is analyzed. An experimental prototype rated for 100 W was used to validate all the findings.
Extensible Lower Bound Function for Dynamic Time Warping

The similarity measurement of time series is a significant approach to mine the rich and valuable law information hidden in the massive time series data. As the most advantageous approach in measuring similarities of time series, Dynamic Time Warping (DTW) has become one of the hottest researches in the field of data mining. However, the DTW algorithm does not satisfy the trigonometric inequality, its time and space complexity are extremely high, how to efficiently realize the retrieval of similar sequences in large-scale sequential sequences remains a challenge. This paper first introduces a novel extensible lower bound function (LB_ex), then validates the effeteness of its lower bound tightness theoretically, finally uses a bidirectional processing strategy (BPS) to reduce computation complexity and time consumption during the massive sequential data retrieval, and significantly improves the operation efficiency. Extensive experiments were conducted with public dataset to evaluate feasibility and efficiency of the proposed approaches. The results show that LB_ex and BPS performs a more robust and efficient processing of similarity of time series than does traditional approaches, reducing by about 43% of time-consuming.
Hybrid Deep Learning Approach For Face Spoofing Detection

For access control in different applications and in their authentication, Face Recognition plays a vital role replacing the traditional password methods. To accurately simulate the details of physical and physiological users, crime experts are developing techniques; these are known as spoofing attacks. Along with the traditional biometric techniques robust counter measures should be introduced to prevent such thefts. For the work, deep features are extracted from the image with the help of the modified CNN (Wavelet CNN). A stacked auto encoder is introduced for spatiality reduction. Along with the hybridisation, type of spoof attack is also detected in terms of printed attack or camera attack. In order to increase the detection rate, a score based prediction is also performed presenting accurate results in finding the type of attack.
Capim-elefante anão sob pastejo I.Produção de forragem

Estudou-se, em 1982, na Universidade da Florida, Gainesville, Florida, E.U.A. , o manejo do capim-elefante anao ( Pennisetum purpureum Schum.) num ensaio de pastejo. Pressao de pastejo (PP) e ciclo de pastem (CP) foram aplicados em cinco niveis: PP-500. 1.000. 1.500, 2.000 e 2.500kg de MS de folha residual ha -1 apos o pastejo e CP-0 pastejo continuo por 14, 28, 42 e 56 dias. Cada ciclo de pastejo incluiu dois dias de pastejo e um periodo de descanso especifico para cada nivel. Usou-se o desenho experimental composto central, nao-rotavel, com treze combinacoes em duas repeticoes. O modelo matematico foi o polinomio completo do segundo grau. As quantidades disponiveis de MS folias e total do capim, a biomassa total, assim como as taxas de crescimento foliar e total do capim e a relacao folha/colmo foram afetadas pela PP e CP. Considerando apenas a producao de MS, recomendam- se para o pastejo do capim-elefante anao PPs baixas (2.000 a 2.500kg de MS de folha residual por hectare) e CPs longos (42 a 56 dias).
Deep Learning for Nanofluid Field Reconstruction in Experimental Analysis

Experiment is an important method to study the thermal and flow performance. Nevertheless, only limited information, such as local temperature and pressure, can be obtained through detection machines. Based on deep learning, a general, useful and flexible reconstruction model is proposed to reconstruct global flow field in two-dimension domain with the limited information exploiting from experiments as input information. Besides, the corresponding performance parameters Nu and $f $ are extracted from generated fields. To validate the feasible, stability and accuracy of the framework, the micro channels with nano fluids are taken as validation case. First of all, the comparison between reconstructed fields and original fields are presented. It shows that reconstructed fields are almost the same as original ones and extracted performance parameters also have high precision. Next, the effects of train size, measuring uncertainty and measuring layouts are considered in sensitivity analysis. Higher train size and smaller measuring uncertainty are advantageous to the reconstruction results. Measuring layout has little influence on reconstruction performance and at least 7 local measuring points are enough.
A Novel Route Planning Method of Fixed-Wing Unmanned Aerial Vehicle Based on Improved QPSO

This paper proposes a quick and accurate method based on an improved quantum-behaved particle swarm optimization (QPSO) algorithm for route planning of fixed-wing unmanned aerial vehicle (UAV). To overcome the deficiencies of local optima and slow global convergence speed, a novel strategy of particle dimension search is proposed in the QPSO algorithm. It is implemented by transforming original evaluation function into evaluation function of waypoint to more easily escape from local optima and accelerate global convergence speed. In addition, an efficient pretreatment technology for the initial trajectory is set to shorten the calculation time of route planning. Compared with other representative route planners, the comparison results indicate that the proposed route planner is more effective and feasible, which can take on faster convergence speed and better global search ability. The proposed route planner can provide a valuable reference for the route planning of fixed-wing UAVs in different environments.
Improve Performance of Indoor Positioning System using BLE

Indoor positioning has grasped great attention in recent years, a lot of research papers have been published in this field. However, there exists no technology that proves its efficacy in all different cases. This paper presents a complete study for indoor positioning with a proposed solution. the first stage of the study is a comparative analysis of several well-known technologies currently available for Indoor Positioning such as Wi- Fi, BLE or GPS. The second stage is based on the comparison's result of the first stage and decide to use Bluetooth Low Energy (BLE) in my solution. BLE is a very practical solution for indoor positioning systems (IPS) which offers acceptable accuracy and low-cost deployment. In this work, I use Received Signal Strength Indicator (RSSI) to estimate the distance between different devices. In the third stage, signal's RSSI are filtered to increase the accuracy of estimated distance using Kalman filter. Finally, trilateration method is used for estimation the position of devices based on the output of previous stages. Experimental results show that the proposed solution has promising results by achieving a positioning accuracy within 0.76 to 0.39m and average 0.53m.
Detecting Deviations in Business Processes Using Process Mining

Monitoring business processes is one of process mining duties, this study aims to detect and diagnose deviations in business processes starting from process discovery to detection and diagnosis as well. The solution is applied to an anonymous Radiology lab in Egypt that focuses on the service process. The data set is about 184622 events that represent 21478 cases extracted from the database according to process mining required attributes. After detecting deviations by applying conformance checking; the results show the deviated cases because of the skipped, inserted, or switched activities. Besides; in some cases, there are overlapped processes that lead to a lot of diagnostics are applied to know deviations causes that influence the process which gives more insights for both all moves on the log and all moves on model for all activities. This finally gives recommendations to control these deviations and to enhance the service process by separating some activities into another process, some cases when doing more one service, or the interaction between instruments and the service process which finally enhanced the service process in the Radiology lab.
Three-Phase Adaptive Frequency Estimator with a Delayed Signal Cancellation Pre-Filter Under Heavily Distorted Grid Conditions

A reliable and precise frequency detection algorithm is useful for different control strategies, especially on microgrid and aerospace applications. The widely-linear adaptive frequency estimator is based on the Augmented Complex Least Mean Square Filter and, thus inherits its fast dynamic response. However, it presents a high susceptibility to input noise, which might present an unexpected behaviour under distorted grid conditions. Therefore, this work proposes a Delayed Signal Cancellation pre-filter stage to eliminate most of the harmonics to achieve fast estimation response and high rejection of harmonic distortions.
Proposal of Fuzzy Controllers for Improve Features of Driven Style in Electric Vehicles Using Experimental Route Data

This document details the proposals of fuzzy controllers for improving the operating characteristics in electric vehicles (EV) through driving styles from experimental data. Fuzzy controllers were designed for following driving styles: aggressive, moderate and conservative; in order to improve the performance and comfort of the EV. For the acquisition of data, urban and extra-urban routes are used from the vehicle ECU through the On Board Diagnostics (OBD) II port. MATLAB simulations support the results obtained, indicating the performance of the proposed controllers, improving the performance and comfort of the EV, according to the selected driving style. Finally, statistical analysis is presented to quantitatively compare the results of the original and controlled signals.
A multi-target tracking platform for zebrafish based on deep neural network

Zebrafish has been widely used in the field of biological behavior, for it is an excellent model organism. Many valuable biological data can be obtained by analyzing the behavioral characteristics of zebrafish. A large amount of data will be generated when studying the behavior of zebrafish. The current artificial statistical methods are inefficient and unpractical. Therefore, it is of great importance to get access to track the behavior of zebrafish automatically and accurately through software. We have proposed a zebrafish multi-target tracking algorithm based on deep convolution neural network to identify the location of multi-zebrafish with high tracking accuracy and speed. The tracking algorithm performs well in different videos with various numbers and sizes of zebrafish. It can greatly improve the efficiency of biological behavioral experiments.
Convolutional Neural Network Based Skin Lesion Analysis for Classifying Melanoma

In a human body skin is the core part, which helps to cover the muscles, bones what's more with the entire body. These days numerous people are suffering from skin diseases. Melanoma is the enormous type of skin malignant growth and the extent of these skin diseases is increasing day by day. Recognizing the type of skin disease automatically from the images can assist in the quick diagnosis and enhanced accuracy saving valuable time. Here, a machine learning based technique using convolutional neural network (CNN) for classifying seven types of skin diseases has been proposed. Transfer learning, along with CNN, has been used to improve the classification accuracy on the International Skin Imaging Collaboration 2018 (ISIC) dataset. Evidence of 11% increase in the accuracy by using transfer learning than using only CNN has been found. Compared to some existing works, performance of this proposed method is promising.
Broadcast Storm Problem in Software Defined Vehicular Network

Vehicular Ad hoc Networks (VANETs) is a self-configuring and decentralized network that is created spontaneously for the communication between neighbouring devices and not relying on any existing infrastructure. The decentralized decisions such as routing and switching in the existing communication system are installed on the same equipment, which is not suitable for networks with characteristics of constantly changing topology and limited transmission range, thus leads to the broadcast storm problem. A novel architecture of networking has been proposed to embrace the dynamic nature of VANETs and suppress the broadcast storm problem by centralized control plane known as software-defined network (SDN), which is having a global view of the network. SDN has limited operational capabilities, as it is often a single network controller based systems. Multiple controllers with self-organization capability provide fault tolerance, handle heterogeneity, the dynamic nature, scalability; collaborative behaviour and the likes are needed. The broadcast storming problem in the software-defined vehicular network has been reviewed comprehensively along with techniques to address it in this paper. Further, various controller placement problems are explored and analyzed to address the broadcast storming problem in multiple controller environment of SDN.