Miracle-dz/UniFAR-FTUs huggingface dataset:
The Research of MOSFET Magneto Transistor Structure by Methods of Device-Technological Modeling

The article describes the experience of device-technological modeling in TCAD of the structure of the MOSFET two-drains magneto transistor. Using numerical calculation the influence of the substrate doping concentration and operating modes to the relative current magnetosensitivity of the MOSFET two-drain magneto transistor is studied. It is shown that the magnetosensitivity function has a maximum in the gate-source voltage range. The optimal of the doping concentration in the substrate for the best magnetic sensitivity was established. The dependence of drain currents and relative current sensitivity on gate-source voltage at different substrate-source voltage is given.
Design of Respiratory Rate Measurement Based on Ultrasound Proximity Sensor

The respiratory rate is one of the five vital signs in human body. The measurement that is most often done is by counting the amount of breath a person does in one minute. This method is considered to be subjective in which each outcome measurement will depend on the counter. Other methods that can be used are by using contact methods, such as strain gauges or impedance methods, transcutaneous CO2methods, probe oximetry (SpO2) methods, and ECG derived respiration rate methods. However, the use of contact methods can cause several problems, such as skin irritation, and surface loading effect. Therefore, in this paper a respiratory rate measurement based on ultrasound proximity sensor is proposed. Measurements are made by calculating the distance change between the front of thoracoabdominal area and the sensor. The results are then processed by peak detection using the Gaussian filter and discrete wavelet transform (DWT). According to the data processing results, the measurement gets the smallest deviation for each error about 3.79 with discrete wavelet transform (DWT) and fast fourier transform (FFT) calculation approach.
Internet of Things Platform for Rapid Development and Learning

Internet of Things (IoT) is the phenomenon of the last few years. Many companies spend huge amount of money in developing the technology for the IoT. The concept of IoT consists of different industrial technologies. One of them is IoT module and device development for rapid prototyping and learning. In our paper, we will describe a platform for rapid prototyping and learning from ON Semiconductor. The integrated development kit and its key shields will be presented. The important part of the IoT is connectivity to the internet and clouds. We will describe out-of-the-box solutions for particular clouds.
Research on DDoS Attack Detection Based on ELM in IoT Environment

There are many Distributed Denial of Service (DDoS) attack accidents in the world, which use the Internet of Things (IoT) devices to launch attacks and make network unavailable such as Mirai. IoT devices have reached more than 8 billion units by 2018 in the world. However, there exist some problems in the IoT environment, such as simple protocols, fixed bandwidth, a lot of devices, and easily controlled. Therefore, IoT devices are vulnerable to intrusion and become a puppet for DDoS attacks. The shortcomings of traditional methods include slow learning speed and low efficiency, and existing DDoS protection methods are applied to Ethernet, not to the IoT environment, so it is vitally important to research how to detect and respond to DDoS attacks in the IoT environment quickly. Based on the characteristics of IoT devices, joint entropy features that consist with DDoS attack in IoT innovatively are extracted and a method based on Extreme Learning Machine (ELM) to detect DDoS attacks is proposed in this manuscript. The experimental results show that the proposed method performs better than other methods in terms of detection accuracy, false positive rate and training duration.
An Intrusion Detection Method Using Few-Shot Learning

Network intrusion detection is an essential means to ensure the security of the network information system. In the real network, abnormal behaviors occur much less frequently than normal behaviors, resulting in scarcity of abnormal samples. We proposed an intrusion detection method based on Few-Shot Learning (FSL), which only used less than 1% of NSL-KDD KDDTrain+ dataset for training, and achieved high accuracy of 92.34% for KDD-Test+ and 85.75% for KDD-Test-21, while other methods, such as J48, Naive Bayes(NB), Random Forest(RF), Support Vector Machine(SVM), recurrent neural network(RNN) and Channel boosted and residual learning based deep convolutional neural network (CBR-CNN), used 20% of KDDTrain+ dataset for training, and achieved relatively low accuracy (less than 89.41% for KDD-Test+ and less than 80.36% for KDD-Test-21). The experiment on dataset of UNSW- NB15 showed a similar result. The detection rates for Dos, U2R, R2L and U2R are improved by our method too, especially for U2R and R2L, which only take up a small proportion of the dataset, the detection rates are increased from 13% to 81.50% and 44.41% to 75.93%, respectively.
Approximate Multiplier Design Using Novel Dual-Stage 4:2 Compressors

High speed multimedia applications have paved way for a whole new area in high speed error-tolerant circuits with approximate computing. These applications deliver high performance at the cost of reduction in accuracy. Furthermore, such implementations reduce the complexity of the system architecture, delay and power consumption. This paper explores and proposes the design and analysis of two approximate compressors with reduced area, delay and power with comparable accuracy when compared with the existing architectures. The proposed designs are implemented using 45 nm CMOS technology and efficiency of the proposed designs have been extensively verified and projected on scales of area, delay, power, Power Delay Product (PDP), Error Rate (ER), Error Distance (ED), and Accurate Output Count (AOC). The proposed approximate 4:2 compressor shows 56.80% reduction in area, 57.20% reduction in power, and 73.30% reduction in delay compared to an accurate 4:2 compressor. The proposed compressors are utilised to implement $8 \times 8$ and $16 \times 16$ Dadda multipliers. These multipliers have comparable accuracy when compared with state-of-the-art approximate multipliers. The analysis is further extended to project the application of the proposed design in error resilient applications like image smoothing and multiplication.
Sequential Multi-Kernel Convolutional Recurrent Network for Sentiment Classification

The emergence of deep learning as a commanding technique for learning heterogeneous layers of feature representations have consequently substituted traditional machine learning algorithms which are generally poor in analyzing compound sentences. Additionally, convolutional and recurrent neural networks have auspiciously yielded state-of-the-art results in sentiment classification and Natural Language Processing (NLP). In this paper, a deep sentiment representation model through the combination of multiple Convolutional Neural Networks (CNN) kernels with Long Short-Term Memory (LSTM) is proposed for sentiment classification. Our model gains word vector representation using pre-trained Global Vectors for Word Representation (GloVe) embeddings, thereafter used as input to the CNN layer which extracts higher local text representations. Finally, Bidirectional LSTM (biLSTM) generates sentiment classification of sentence representation based on context dependent features. Our combined approach of CNN and biLSTM was experimented using the Stanford Large Movie Review Dataset (IMDB) and Stanford Sentiment Treebank Dataset (SSTB) for binary classification. The evaluation achieves outstanding results in outperforming several existing approaches with 90.4% accuracy on the Stanford Sentiment Treebank dataset and 94.8% accuracy on the Stanford Large Movie Review dataset. These results are achieved with a drastic reduction of model parameters and without a pooling layer in the CNN architecture, helping to retain local and structural information in comparison to other existing deep neural network frameworks. (Abstract)
Accurate and efficient measurements for complex permittivity of 3D printer filaments using a 50GHz band TM010 mode cavity resonator

An additive manufacturing technique with an FDM type 3D printer is an attractive and novel fabrication technology to develop microwave and millimeter wave components. Nevertheless there are few reports for complex permittivity of 3D printer filament materials. We have been presented a thin dielectric rod measurement method using a 50GHz band TM010mode cavity resonator and an improved perturbation formula corrected with FEM simulations. However, this method takes long time due to a lot of FEM calculations. In this paper, an accurate and efficient complex permittivity measurement method for a thin dielectric rod is proposed using the 50GHz band TM010 mode cavity resonator based on a rigorous analysis by a mode matching method. In addition, the measured results of complex permittivity for three kinds of 3D printer filaments are shown at 45GHz band.
Severe Hyponatremia During Moxifloxacin Treatment: A Case Report

In this report, a case of severe hyponatremia associated with moxifloxacin is presented. A ninety-year-old woman was hospitalized with pneumonia. On the first day of hospitalization, blood sodium was 141 mmol/L. Persistent hyponatremia (123127 mmol/L) began in the second day of moxifloxacin therapy. It did not improve despite the use of hypertonic fluids throughout the two-week treatment. The therapy was discontinued on the 13th day with moxifloxacin as the cause of hyponatremia in mind. Next day, the blood sodium of 127 mmol/L was detected. Gradually increasing levels were monitored and one week after discontinuation of therapy normal results were obtained. It should be kept in mind that hyponatremia may develop during the use of moxifloxacin, especially in patients with additional risk factors. Klimik Dergisi 2017; 30(3): 153-4.
Convolutional Neural Network Method for Particle Selection of Cryo-EM Micrographs

Cryo-electron microscopy(cryo-EM) has been widely used in structural biology. Molecular particle selection in cryo-EM micrographs as an important intermediate affects the quality of 3D reconstruction. A new method called RSelector is proposed with a new feature extraction network based on characteristics of cryo-EM micrographs and two sub-networks for classification and coordinate regression. Experiments on the three public cryo-EM datasets show that with less manual interference in the training stage compared with the existing common methods, the RSelector method achieves a better selection performance and also performs well in terms of resistance to label noise.
Batched Trajectory Compression Algorithm Based on Hierarchical Grid Coordinates

In the visual analysis application of large-scale off-line trajectory data, users need fast user-interface response with low latency. For such demand, we established a hierarchical grid coordinate system and proposed a batched trajectory compression algorithm. We projected the original track points into the discrete grid coordinates at the corresponding layer according to the trajectory visualization needs. Then, the original trajectory points were clustered by using the approximation of transformation to form a preliminary data set of compressed trajectory points in the corresponding layer. The data set was processed by the Douglas-Poker algorithm to get the final compressed trajectory data; we also accelerated the algorithm by using GPU. The analysis and experiments show that our algorithm can quickly generate the compressed trajectory data and improve the displaying efficiency of massive trajectory data on the basis of maintaining the invariance of the trajectory data visualization.
A Novel 1-Bit Dual-Linear Polarized Reconfigurable Transmitarray Element Using Double-Layer Dipoles

The design and simulations of a Ku-band 1-bit dual-linear polarized reconfigurable transmitarray antenna (RTA) element is presented. The proposed element consists of a double-layer dipole and two vertical vias, which connect the upper and lower metallic layers and augment the transmission magnitude and phase shift range. By controlling the PIN diodes integrated on the dipoles, the proposed element operates in two different modes, leading to a 180° phase difference. The dual linearly polarization is achieved by orthogonally placing two sets of dipoles in an interlaced grid, and each polarization can be independently controlled. The proposed 1-bit RTA element is simulated to obtain a clear out-of-phase transmission, and the transmission magnitude loss is 1.7 dB and 2.1 dB for two element states.
Big-Data-Driven and AI-Based Framework to Enable Personalization in Wireless Networks

Current communication networks use design methodologies that prevent the realization of maximum network efficiency. In the first place, while users' perception of satisfactory service diverges widely, current networks are designed to be a "universal fit," where they are generally over-engineered to deliver services appealing to all types of users. Also, current networks lack user-level data cognitive intelligence that would enable fast personalized network decisions and actions through automation. Thus, in this article, we propose the utilization of AI, big data analytics, and real-time non-intrusive user feedback in order to enable the personalization of wireless networks. Based on each user's actual QoS requirements and context, a multi-objective formulation enables the network to micro-manage and optimize the provided QoS and user satisfaction levels simultaneously. Moreover, in order to enable user feedback tracking and measurement, we propose a user satisfaction model based on the zone of tolerance concept. Furthermore, we propose a big-data-driven and AI-based personalization framework to integrate personalization into wireless networks. Finally, we implement a personalized network prototype to demonstrate the proposed personalization concept and its potential benefits through a case study. The case study shows how personalization can be realized to enable the efficient optimization of network resources such that certain requirement levels of user satisfaction and revenue in the form of saved resources are achieved.
Enhanced UE Slice Mobility for 5G Multi-RAT Networks

The Third Generation Partnership Project (3GPP) Fifth Generation (5G) networks employ network slicing in order to implement multiple service types corresponding to different business needs. Network slicing results in the creation of multiple end-to-end logical networks over shared physical infrastructure. User Equipments (UEs) can use multiple network slices concurrently in order to avail a diverse set of services. In order to support UE slice mobility, especially for UEs accessing multiple slices, 3GPP mandates that all supported slices should be deployed homogeneously within a Registration Area (RA). However, studies have shown that the deployment cost of the network increases with the increased number of slices. This makes it a costly proposition to deploy all supported slices on every Radio Access Network (RAN) node within the RA. In this paper, we propose that the slice deployment requirements can be more flexible while ensuring UE slice mobility through the use of an enhanced multi-connectivity protocol. The proposed protocol can also be used for ensuring slice mobility across multi-Radio Access Technology (RAT) networks, including 5G New Radio (NR) and Wireless Local Area Networks (WLANs). The performance improvements obtained due to the proposal are demonstrated through simulations in network simulator-3 (ns-3).
A Novel Circular Polarization Series-Fed Conformal Traveling-Wave Array(TWA)

A new design of a traveling wave conformal array is proposed and presented for parallel-fed and series-fed shceme. The circular polarization is achieved by adding four exciting ports with 90 degrees exceeded to each other, which is easy in parallel-fed array. While in series-fed array, in order to obtain circular polarization, the phase delay of element should be set at 90 degrees or −90 degrees by the selection of the length of the feed line. The element is a well done octagonal patch with four centrosymmetric $\Psi$-shaped slots antenna proposed in AP-S 2018. The structure of the antenna array and feed is studied and simulated with HFSS on computer.
Towards a Mobile and Fast Melanoma Detection System

Early detection of melanoma is crucial to avoid skin cancer deaths, but only with the recent advances of deep convolutional neural networks architectures, such as MobileNet, it is possible to create a reliable enough system to detect melanoma, that can be implemented on resource constrained environments such as mobile phones or embedded systems. With this aim, this work assesses the performance of the implementation of an early melanoma recognition system using MobileNet trained from the HAM10000 database. Besides, we explain in detail two strategies to improve melanoma classification task, i.e., data augmentation on an unbalanced dataset and a multiclass approach to address a binary classification problem. Numerical results in terms of AUC metric and ROC curves corroborate the validity of our model. The performance of the proposed model is also compared to the average dermatologist performance.
Investigation of an UWB Antipodal Tapered Slot Antenna Element Based on Substrate Integrated Waveguide in an Antenna Array

The calculated characteristics of a two-dimensional small-sized 4x4 antenna array in the frequency range of 18 to 42 GHz are presented. The array consisted of ultra-wideband high gain tapered slot antenna elements based on substrate integrated waveguide. Significant effect of the relative position of the antenna array elements operating in an ultra-wide frequency band on array’s characteristic is shown. The relative position choice allowed to eliminate grating lobes in the upper part of the frequency as well as remove gain stray notches occurring in the frequency range of 21 to 27 GHz. Calculated antenna array radiation patterns in E- and H- planes while steering are presented. Changes in the side lobe level and the expansion of the main lobe of the radiation pattern while steering are shown.
Low-Cost Continuous-Wave Radar System Design for Detection of Human Respiration Rate

Radar systems for medical applications are widely researched and developed. One application of this radar is to measure human vital conditions such as respiratory rate. Therefore, a system that can detect human respiratory rate is in need. In this study, a continuous-wave radar was designed with a LoRa RFM95 as a transmitter at 862–1020 MHz frequency and RTL-SDR as a receiver as well as an analog to digital converter. The antenna used in this study are two single elements rectangular microstrip patch antennas, each for transmitting antenna and for receiving antenna designed center frequency of 915 MHz. The methods used to obtain human respiratory rate are complex demodulation magnitude. Experiment shows that this radar can obtain human respiration successfully.
Improving student reading comprehension of learning materials using the analysis of Eye-Tracking data

The paper deals with the creation of effective e-learning courses, the task which in many cases is non-trivial. Many currently in use e-courses do not meet the principles of e-course creation, thus significantly reducing their effectiveness. In order of making it easier to identify deficiencies, we have decided to design an experiment using eye-tracking technology to pinpoint key areas in which it is necessary to enrich the e-courses with interactive multimedia elements, explanatory notes, etc. In the first part of the experiment, we will use a combination of questionnaires, personal interviews, and eye-tracking methods to identify gaps, or any suggested improvements to the content of the courses. Subsequently, we will adjust the e-courses created in this way according to the feedback received, following the principles of e-course creation described in the literature. We will verify the result with an experiment in which the control group will study using the original e-courses, while the new courses will be made available to the experimental group.
EPAW: Efficient Privacy Preserving Anonymous Mutual Authentication Scheme for Wireless Body Area Networks (WBANs)

The recent advancement in wireless body area networks (WBAN) plays an important role in remote health care systems. However, these networks are suffering from data security and privacy threats. Lack of anonymous authentication and secure data communication leads to operation failure in WBAN. Computational cost and privacy preservation are the two major hindrances for anonymous authentication in many existing schemes. Therefore, a secure and efficient privacy-preserving anonymous authentication scheme is proposed to provide data security and privacy to the users with low computational and communication cost. The performance analysis and experimental simulation results ensure that our proposed anonymous authentication method outperforms the existing systems in terms of providing data security and privacy with less computational overhead.
Implementation of 1T1R-based OxRRAM Memristor Model for Circuit Design and Simulation

The new emergent memristor can provide non-volatile memory storage, but also an intrinsic computation for matrix-vector multiplication, ideal for low-power, high-speed data analysis accelerators carried out in memory. Several memristor models have been investigated up to now. Although, a compact model needs to be flexible, general, and sufficiently accurate. In this paper, a comparative analysis of a proposed Verilog-A and SPICE memristor models is discussed, in order to elaborate an appropriate model for implementation in CMOSbased circuit applications. An analysis of a Verilog-A memristor model is discussed in order to check the model behavior in ITIR OxRRAM configuration and validating the comparison at circuit level. Two cases are considered using this cell either as a PROM or as an EEPROM. Hence, this analysis generated using Spectre circuit simulator. Our simulation results carry the desired nonlinear memristor fingerprint, and the applicability to fit and simulate different switching behaviors. These results are verified by both electrical and experimental characterization data.
Application of Machine Learning in the Development of Game Artificial Intelligence Modules

This research is an analysis of the work of artificial intelligence in the field of the transport systems management, guided by the optimal economic strategy and based on the example of the game-simulator Open Transport Tycoon Deluxe. During the analysis, a self-learning program was being created for this game. The results were being evaluated and based on the selected test situations. In conclusion is given examples of use of machine learning in various fields of activity.