Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting the Possibility of Being Malignant Tumor based on Physical Symptoms using IoT

This paper presents a novel technique for predicting the possibility of being malignant brain tumor based on human physical symptoms using internet of things. Other state-of-the-art techniques for detecting the malignant brain tumor based on MRI images use X-ray light to detect malignant tumor, which is more expensive and harmful for health. On the other hand, the proposed device is designed in a portable way to monitor the real-time heart rate, blood pressure, body temperature and also more suitable for predicting the possibility of being malignant tumor compared to other existing techniques. This study also shows the relationship between symptoms and sub-symptoms of malignant brain tumor. The effectiveness of the proposed method for predicting the possibility of being malignant tumor from physical symptoms is testified by the result analysis.
ECG Signal Processing Algorithms to Determine Heart Rate

The paper describes the processing of ECG signals, which consists of preliminary processing of the original ECG signals, frequency filtering of signals, extraction and calculation of features for determining the heart rate. Provides brief information on the physiological and pathological reactions of the cardiovascular system. The main parameters of heart rate variability have been determined. Algorithmic and software implementation of signal processing and R-peak detection and calculation of RR-intervals in the PYTHON environment are proposed.
An ISAR-SAR based Localization Method using Passive UHF RFID System with Mobile Robotic Platform

A novel RFID synthetic aperture radar (SAR) based localization method using an antenna trajectory estimated using known reference tags of known location is proposed and demonstrated to find the location of target tags who’s location is not known. A robot with an integrated RFID reader and antenna is used to obtain phase measurements of both the reference and target tags in an indoor environment. The trajectory of the moving RFID antenna is estimated using an Inverse SAR (ISAR) sensing algorithm. The trajectory is then combined with the phase measurements of the target tags to compute their location using a conventional SAR algorithm. The performance is compared to target tags located by SAR where the antenna trajectory is measured by LiDAR. Experimental results show similar performance (15cm mean absolute error) using both LiDAR-SAR and ISAR-SAR algorithms. Compared to previous SAR based systems, the ISAR-SAR based RFID system is a cost-effective solution which is more commercially attractive for inventory tracking applications.
Scalable, Physical Effects Measurable Microgrid for Cyber Resilience Analysis (SPEMMCRA)

The ability to advance the state of the art in automated cybersecurity protections for industrial control systems (ICS) has as a prerequisite of understanding the trade-off space. That is, to enable a cyber feedback loop in a control system environment you must first consider both the security mitigation available, the benefits and the impacts to the control system functionality when the mitigation is used. More damaging impacts could be precipitated that the mitigation was intended to rectify. This paper details networked ICS that controls a simulation of the frequency response represented with the swing equation. The microgrid loads and base generation can be balanced through the control of an emulated battery and power inverter. The simulated plant, which is implemented in Raspberry Pi computers, provides an inexpensive platform to realize the physical effects of cyber attacks to show the trade-offs of available mitigating actions. This network design can include a commercial ICS controller and simple plant or emulated plant to introduce real world implementation of feedback controls, and provides a scalable, physical effects measurable microgrid for cyber resilience analysis (SPEMMCRA).
On the Applicability of Hypothesis Testing to On-board Detection of an Inter-Turn Short Circuit Fault in PMSM Drive

The demage caused by an Inter-turn short circuit (ITSC) in electric motors is well know. Futhermore, the fact that it’s one of the most critical electrical faults in a Permanent Magnet synchronous machine (PMSM) makes its on-board detection increasingly important. On the other hand, the use of data-driven fault detection methods has gained great interest due to the availability and access to large amounts of data by intelligent sensors. In this perspective, this paper deals with the fault detection problem of an ITSC in a PMSM within the statistical framework of hypothesis testing. This approach does not require high computational effort and can be implemented in low-cost control units using elementary mathematical operations and look-up table for test statistics.
A Malware Similarity Analysis Method Based on Network Control Structure Graph

Recently, graph-based malware similarity analysis has been widely used in the field of malware detection. However, the wide application of code obfuscation, polymorphism, and deformation changes the structure of malicious code, which brings great challenges to the malware similarity analysis. To solve these problems, in this paper, we present a new approach to malware similarity analysis based on the network control structure graph (NCSG). This method analyzed the behavior of malware by application program interface (API) association and constructed NCSG. The graph could reflect the command-and-control(C&C) logic of malware. Therefore, it can resist the interference of code obfuscation technology. The structural features extracted from NCSG will be used as the basis of similarity analysis for training the detection model. Finally, we tested the dataset constructed from five known malware family samples, and the experimental results showed that the accuracy of this method for malware variation analysis reached 92.75%. In conclusion, the malware similarity analysis based on NCSG has a strong application value for identifying the same family of malware.
Proactivity and Leadership Predictors in University Students of the Business Administration Career

The purpose of this study was to identify the relationship between predictor factors of proactivity and leadership in university students of the career of business administration. The sample was of 152 students of the career of business administration 82 men (53.9%) and 65 women (42.8%). The sample were between the 16 to 44 years old (MeanAge = 18.86, SD = 3.70) and of they 112 (74%) were of capital city (Lima-Peru) and 33 (22%) were of province. In the results of the analysis of correlations, the mother’s education and father’s education demonstrated a light positive relationship with the leadership. Finally, proactivity demonstrated a strong and positive relationship with the leadership. Finally, the age predicts negatively the proactivity, and proactivity predicts positively the leadership.
A 5.8-GHz Phased Array System Using Power-Variable Phase-Controlled Magnetrons for Wireless Power Transfer

We build a phased array system with four power-variable phase-controlled magnetrons (PCMs) by applying the injection-locking method and phase-locked-loop method. To reduce the cost and ensure the durability of the phased array, a waveguide slot array antenna was designed and used for the output antenna of power-variable PCMs. The slot antenna has an expected angle deflection of 22.5°, a gain of 24.9 dBi, and the half bandwidth of the main lobe was 10°. We demonstrated the properties of microwave beamforming and wireless power transfer based on the magnetron phased array system. In horizontal directions, a beam scanning range of ±3° was obtained by adjusting the output phase of the magnetrons. Furthermore, the received dc power reaches 142 W at a distance of 5 m when the output microwave power of the magnetron phased array is 1304 W.
Vision-based motion estimation for vehicles on test track via cone markers

In this paper, a vision-based motion estimation algorithm based on the ORB feature detector is presented. The mono camera is mounted facing forwards and the method is not utilized any aprior motion model. Supplementing the general detection-match-pose estimation steps, an iterative loop is proposed to eliminate the problems of the vision-based technique and increase the accuracy. The method operates with vehicle dynamic based assumptions since the sensor is mounted to a car-like ground vehicle. The motion estimation results can be used for orientation smoothing, sideslip estimation, or sensor calibration.
Development of an integrated wristband for physical activity and pulse measurement

For measuring human daily physical activity and exercise in real time, a wristband was developed in this study, which can measure physical activity and exercise pulse at the same time together. The Wristband can provide data in three aspects: (1)PA subsystem for measuring physical activity; (2) EP subsystem for measuring Exercise Pulse; (3) HF subsystem for measuring health fitness and instruction feedback. By comparing the Counts Per Minute data of the subjects with the actigraph gt3x & tri-axial accelerometer, the difference was less than 5%; the pulse data measured by wristband and polar heart rate band were within −2.12 ∼ +2.03 beat per minute and the relative error is between −1.65% ∼ +1.39% under different exercise types and different speed intensities. Conclusion: the measurement accuracy of wristband can meet the requirements of exercise pulse measuring, and it can satisfy measurement and evaluation of physical activity.
Spatially modulated alpha-band activity does not mediate tactile remapping and fast overt orienting behavior

Posterior oscillatory alpha-band activity is commonly associated with spatial-attentional orienting and prioritization across sensory modalities. It has also been suggested to mediate the automatic transformation of tactile stimuli from a skin-based, somatotopic reference frame into an external one. Previous research has not convincingly separated these two possible roles of alpha-band activity. In particular, the use of delay paradigms, implemented to allow temporal evolution of segregable oscillatory brain responses to stimulus, motor planning, and response, have prohibited strong conclusions about a causal role of oscillatory activity in tactile-spatial transformations. Here, we assessed alpha-band modulation with massive univariate deconvolution, an analysis approach that disentangles brain signals overlapping in time and space. Thirty-one participants performed a delay-free, visual serial-search task in which saccade behavior was unrestricted. A tactile cue to uncrossed or crossed hands was either informative or uninformative about visual target location. Alpha-band suppression following tactile stimulation was lateralized relative to the stimulated hand over centro-parietal sensors, but relative to its external location over parieto-occipital sensors. Alpha-band suppression reflected external touch location only after informative cues, challenging the proposition that posterior alpha-band lateralization indexes automatic tactile transformation. Moreover, alpha-band suppression occurred ~200 ms later than externally directed saccade responses after tactile stimulation. These findings suggest that alpha-band activity does not play a causal role in tactile-spatial transformation but, instead, reflects delayed, supramodal processes of attentional re-orienting.
On the Instant Iterative Learning MPC for Nonlinear Systems

Model predictive control (MPC) is one of the methods which optimizes the trajectory of the system with the constraints from predicted states of the system. A number of researches have studied its applications, for example, online optimization methods and fast solvers for nonlinear systems, because of its effectiveness. We propose one of the methods to apply online MPC to nonlinear systems based on instant MPC (iMPC). We recast iterative learning MPC (ILMPC) for nonlinear systems as iMPC via the primal-dual gradient algorithm, which we name "i-ILMPC". Finally, a numerical simulation is performed to demonstrate its effectiveness.
Assessing Mock Classes: An Empirical Study

During testing activities, developers frequently rely on dependencies (e.g., web services, etc) that make the test harder to be implemented. In this scenario, they can use mock objects to emulate the dependencies’ behavior, which contributes to make the test fast and isolated. In practice, the emulated dependency can be dynamically created with the support of mocking frameworks or manually hand-coded in mock classes. While the former is well-explored by the research literature, the latter has not yet been studied. Assessing mock classes would provide the basis to better understand how those mocks are created and consumed by developers and to detect novel practices and challenges. In this paper, we provide the first empirical study to assess mock classes. We analyze 12 popular software projects, detect 604 mock classes, and assess their content, design, and usage. We find that mock classes: often emulate domain objects, external dependencies, and web services; are typically part of a hierarchy; are mostly public, but 1/3 are private; and are largely consumed by client projects, particularly to support web testing. Finally, based on our results, we provide implications and insights to researchers and practitioners working with mock classes.
Robot Localization via Passive UHF-RFID Technology: State-of-the-Art and Challenges

This paper presents a state-of-the-art analysis on the current methods for robot localization based on the passive UHF-RFID technology. The state-of-the-art analysis describes the main features and challenges of several localization methods. Then, a first experimental analysis related to a novel phase-based robot localization method is presented. The robot on-board reader collects phase data from a set of passive reference tags during its motion, so resembling to a synthetic array. Then, the phase data are combined with information acquired by low-cost kinematic sensors, through a Sensor Fusion approach. The experimental results show that centimetre order localization errors can be achieved in a typical office indoor scenario by employing a few reference tags.
Boltzmann-Based Distributed Replicator Dynamics: A Smart Grid Application

Multi-agent systems (MAS) have been recently used to overcome problems in the control field. The development of distributed control techniques has proven to be an effective approach capable of addressing issues such as communication constraints between agents, modeling of scenarios closer to reality, use of high computational requirements, and understanding of the system complexity. Game theory supports the understanding of interactions between agents, where strategies play a determinant role to maximize agent outcomes. Evolutionary game theory (EGT) has introduced the concept of biological evolution, which has proven to deal with applications that can be modeled using population dynamics.In this work, we consider the replicator dynamics (RD) and propose the use of the Boltzmann model as a base to introduce the Boltzmann-Based Distributed Replicator Dynamics (BBDRD). This novel approach can deal with engineering applications, where the modeling of the system needs no complete information to find the outcome. This proposal allows obtaining a more realistic scenario and gives another perspective to tackle issues with partial information. To understand the behavior of the BBDRD, its use in a smart grid application is validated.
Artificial intelligence application to improve the performance of distance learning servers during the coronavirus pandemic threat period

As of early 2020, almost all countries are fighting the coronavirus pandemic by implementing the rigorous restrictions recommended by the World Health Organisation to help reduce the number of infections as much as possible. The restrictions also apply to members of the academic community. The universities have suspended all teaching activities - except online. Most universities have introduced solutions for distance learning. However, organisation of distance education requires appropriate technological infrastructure. Providing the right IT infrastructure is not an easy challenge, because network devices and network servers note record-breaking peak loads during this time. It seems that there are potentially many possibilities of using artificial intelligence to improve the performance of distance learning platforms, information systems and network infrastructure in this difficult and demanding period. Examples of such use of artificial intelligence applications are presented in this article. The paper shows that the use of artificial intelligence to improve the operation of distance learning servers is potentially possible in many areas. It is also worth noting that the application of artificial neural networks and LSTM neural networks for this purpose seems very promising. The presentation of sample experiments and obtained results in this article seems to confirm this thesis.
Short-time traffic flow forecast with weather characteristics

In order to solve the problem that the traffic flow prediction model only focuses on the time dimension, which results in the model’s low accuracy. In this paper, the three most influential weather features are extracted from the weather data through the principal component analysis (PCA). Weather features are extracted by PCA algorithm. The combined input of weather and time characteristics is compared with the input of time characteristics to obtain the simulation results. Experimental simulation results show that the RMSE and MAPE of the model decreased to 11.9844% and 7.1398 respectively when the extracted weather characteristics were used as the input of LSTM. The construction of the model can optimize the travel experience and facilitate the traffic department to grasp the traffic flow.
A Review On Image Processing Techniques

In current circumstance image processing is one of interesting field. It is methodology which is normally used to improve picture which are gotten since many resources. This work gives outline of image processing techniques. Fundamental apprehension of this work is characterizing different methods utilized in various periods of image processing. Gathering of pixels dependent on specific sort of similitude or brokenness among the pixel called Segmentation. Segmentation of ROI from the given info picture decides the accomplishment of investigation. Legitimacy measurements assists with estimating the similitude of the sectioned picture result. Most significant and required for human endurance is food. In that situation Agriculture industry plays an indispensable job and the business faces lose in light of specific reasons. One of the motivations to yield lose is unconscious of infection conclusion and more often than not rancher can foresee malady finally second. By actualizing innovative improvement in agribusiness industry attempt to improve the harvests lose and that outcomes expanding rancher salary. Files based power histogram segmentation strategy used to portion the sickness influenced part from undesirable leaf with better exactness rate. Segmentation is significant stage in picture handling procedure and it assists with diagnosing the sick locale.
Analysis of Stator Cooling Methods of Fault-Tolerant Electric Machines

This paper presents a study and determination of the effective stator cooling system of the fault-tolerant electric machines considered mass-and-size parameters and the efficiency of the loss removal in copper. The object of study is a 14.7-kVA 57000-rpm fault-tolerant electric machine. The varieties of the cooling system with silicone cooling tubes (cooling tubes located on the end windings; cooling tubes located in the stator slots; cooling tubes formed a cooling jacket in which the phase coil is located) and the cooling system with an oil-immersed stator were compared in order to assess the efficiency of each system. The presented results may be useful to scientists and engineers who are engaged in the design of fault-tolerant electric machines, as well as to employees of enterprises that produce fault-tolerant electric machines.
A Concept Schema of a Portable IoT-Sensor System for Smartphones

Many new buildings have already a system in which user can adjust the temperature. Some of them have even a temperature sensor or wireless monitoring and control system via Internet of Things. These systems are typically fixed and cannot be moved. For older buildings, adding this kind of system can be expensive because this often requires remolling in addition of the cost of the system itself. Moreover, renters typically prefer to invest in systems which they can take with them. In addition, users may need other kind of sensor data and for different applications. There is therefore a need for a portable, relatively inexpensive system which should be easy to use with a smartphone. In this paper, a concept schema is presented for a such system with a case example.
Comparison of Electric Bus Power Consumption Modelling and Simulation Using Basic Power Model, ADVISOR and FASTSim

Modelling tools for electric vehicle power consumption simulation can range from simple, fast, less accurate models requiring few inputs to slow, highly complex, accurate models requiring many detailed inputs. Models for real-time applications like electric vehicle range estimation need near instantaneous computation time. ADVISOR and its successor, FASTSim are vehicle simulators developed by US National Renewable Energy Lab. They are sufficiently sophisticated as design tools but simulation time can be slow for real time applications. The aim of this paper is to develop a Basic power consumption Model in Simulink and benchmark its speed and accuracy against these two simulators. Real-world driving profiles obtained from experiments conducted on an electric bus were used for validation. Results show that Basic Model has comparable spread of accuracy compared to both simulators while having significantly faster computation time. It can be concluded that a Basic Model could provide sufficient accuracy for real-time application.
Efficient Spectrum Sensing for the Relay Based Cognitive Radio Network for Enhancing the Network Coverage for Wireless Patient Monitoring System

The wireless Patient Monitoring System (PMS) is facing the issue of spectrum congestion. The 5G technology based wireless PMS is a promising solution. With the advancement in the wireless telecommunication technology from 4G to 5G, the important paradigm to be considered for the state of the art technique for the 5G/6G is the device to device (D2D) communication. One of the important issue associated with the advanced wireless communication technology is the network disconnection due to network blockage. Moreover, inefficient usage of the spectrum is the another major concern in wireless communication services. In this paper, by efficiently utilizing the unused spectrum the Cognitive Radio (CR) Relay assisted cooperative D2D communication has been aimed to enhance the network capacity (data rate) of the User Equipment (UE)/Licensed User (LU) which is in the network blockage area for wireless remote PMS. The CR enabled with Modified Whale Optimization Algorithm (MWOA) has been proposed for efficient spectrum sensing. The CR based relay technique for D2D communication with UE can significantly improve the performance of the network capacity without any investment on deploying new base stations. Simulation results exhibits that the performance of the network with MWOA-CR based relay technique has significantly improved.