Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that researchers have proposed. Thus, the purpose of this systematic literature review is to investigate the available quality assessment methods that researchers have utilized to evaluate the performance of the edge detection algorithms. Due to the vast number of available literature in this area, we limit our search to only open-access publications. A systematic search in five publisher websites (i.e., IEEExplore, IET digital library, Wiley, MDPI, and Hindawi) and Scopus database was carried out to gather resources that are related to the edge detection algorithms. Seventy-three publications that are about developing or comparing edge detection algorithms have been chosen. From these publication samples, we have identified 17 quality assessment methods used by researchers. Among the popular quality assessment methods are visual inspection, processing time, confusion-matrix based measures, mean square error (MSE)-based measures, and figure of merit (FOM). This survey also indicates that although most of the researchers only use a small number of test images (i.e., less than 10 test images), there are available datasets with a larger number of images for digital image segmentation that researchers can utilize.
A Deep-Learning Approach for the Prediction of Mini-Mental State Examination Scores in a Multimodal Longitudinal Study

This study introduces a new multimodal deep regression method to predict cognitive test score in a 5-year longitudinal study on Alzheimer’s disease (AD). The proposed model takes advantage of multimodal data that includes cerebrospinal fluid (CSF) levels of tau and beta-amyloid, structural measures from magnetic resonance imaging (MRI), functional and metabolic measures from positron emission tomography (PET), and cognitive scores from neuropsychological tests (Cog), all with the aim of achieving highly accurate predictions of future Mini-Mental State Examination (MMSE) test scores up to five years after baseline biomarker collection. A novel data augmentation technique is leveraged to increase the numbers of training samples without relying on synthetic data. With the proposed method, the best and most encompassing regressor is shown to achieve better than state-of-the-art correlations of 85.07%(SD=1.59) for 6 months in the future, 87.39% (SD =1.48) for 12 months, 84.78% (SD=2.66) for 18 months, 85.13% (SD=2.19) for 24 months, 81.15% (SD=5.48) for 30 months, 81.17% (SD=4.44) for 36 months, 79.25% (SD=5.85) for 42 months, 78.98% (SD=5.79) for 48 months, 78.93%(SD=5.76) for 54 months, and 74.96% (SD=7.54) for 60 months.
Doped GNR p–n Junction as High Performance NDR and Rectifying Device

On the basis of density functional theory and nonequilibrium Green’s function technique, we have presented theoretical results on transport properties of B- and N-doped aGNR p–n junction. The current–voltage characteristic of this system indicates robust negative differential resistance (NDR) behavior of it. Meanwhile, that p–n junction diode can be utilized as a highly efficient voltage rectifier. The calculations also reveal that the voltage rectifying efficiency can be highly enhanced by forming a tandem diode by connecting two single diodes in series. The variation of transport properties on the width of aGNR is also investigated. The NDR phenomena as well as the rectifying property can be well explained on the basis of relative shifting of discrete energy states of the conjugate system with applied bias, which in turn explains very strong coupling between the p and n regions of the diode.
An accelerating technique by same meshes in FEM

An accelerating technique by same meshes in finite element method(FEM) is introduced. By the characteristic of base function depending on geometric form the computing quantities of base function and coefficients matrix are reduced by constructing the same meshes. And farther coefficients matrix of fem quick filling is realized and the quick solving of aim problem is achieved as well. The algorithm is demonstrated by eigen modes solving of the rectangle filling waveguide. The novel method can be extended to the fem solving of other problems.
2-MHz Compact Wireless Power Transfer System With Voltage Conversion From 400 V to 48 V

In many industrial applications, wireless power transfer (WPT) systems with small transmitting and receiving coils and compact secondary sides are required. This asks for high switching frequencies to achieve sufficiently large power densities. At the same time, a conversion from higher to lower voltages is often desirable, and thus different transmitting and receiving coils must be used. This work presents an inductive WPT system addressing both challenges. It uses small transmitting and receiving coils with a surface area of only 20 cm2, achieving a power transfer of 500W. The design of the coils allows for a voltage conversion from 400V to 48 V. To simplify the receiving circuit, operation is similar to an LLC converter so that the compensation capacitor on the secondary side can be omitted. First experimental results of a prototype system show a peak efficiency of 92% at 2 MHz while transmitting 400 W.
Novel Method Enabling Forward and Backward Propagations in NAND Flash Memory for On-Chip Learning

In this work, a novel synaptic array architecture enabling forward propagation (FP) and backward propagation (BP) in the NAND flash memory is proposed for the first time for on-chip learning. In the proposed synaptic architecture, positive synaptic weight and negative synaptic weight are separated in different arrays to enable weights to be transposed correctly. In addition, source-lines (SLs) are separated, which is different from the conventional NAND flash memory, to enable both the FP and BP in the NAND flash memory. By applying input and error input to bitlines (BLs) and string-select lines (SSLs) in NAND cell array, respectively, accurate vector–matrix multiplication is successfully carried out in both FP and BP eliminating the effect of pass cells. At a read voltage of 2 V, the inference accuracy of 95.58% which is comparable to that of 95.81% obtained with perfect linear device is achieved. The proposed on-chip learning system is much more robust to weight variation compared to the off-chip learning system. Finally, superiority of the proposed on-chip learning architecture is verified by circuit simulation of a neural network.
Brain Tumor Detection: An Application Based on Machine Learning

Since years Machine Learning have played a significant part in the healthcare sector. Out of the various deep learning approaches, Convolutional Neural Networks has shown the most promising results in the classification task. The objective of this paper was to build an application of CNN by proposing a medical expert-based approach to detect Brain Tumor. Annually thousands of people are being diagnosed with Brain Tumor and various treatments such as surgery and radiotherapy are being used currently and to detect the tumor MRI is the imaging technique widely used which is very time-consuming as tumors are highly variable in their size, shape, and structure. Also, there is a risk of human error as there may occur variations by different expert physicians leading to inaccurate results which call for the need for an automatic detection system. The proposed approach helped to classify the MRI images as tumorous and non-tumorous, restraining the visual exploration by doctors to make it less time-consuming since the methods used in the system can detect tumors with the use of MRI images. The architecture was based on CNN along with Transfer Learning model VGG-16. The proposed system contained a dataset from Kaggle which was then preprocessed followed by augmentation and passed to the architecture where it was capable of obtaining accurate results rapidly.
Machine Learning Attack on a Multiplexer PUF Variant Using Silicon Data: a Case Study on rMPUFs

The security of Physical Unclonable Functions (PUFs) remains central and persists as an obstacle to the applicability of some designs. A Multiplexer PUF variant called (rMPUF) was recently proposed to enhance the security of the delay-based PUFs, which they show that the design is secure against machine learning attacks based on simulated data. In this paper, we take the analysis on rMPUFs to the next level by implementing these designs on field-programmable gate arrays (FPGAs) and evaluate their resistance against modeling attacks using silicon CRPs. Our risk analysis on rMPUFs with up to seven MUXs stages using different challenge sizes show that all designs are vulnerable to machine learning attacks. Security enhancements are modest and come at the high cost of hardware overhead. Nevertheless, the proposed rMPUFs can be a good candidate for weak PUFs with access-restricted protocols, considering the stability of the responses that do not deteriorate with enhancements in the designs' security.
Smart Grids Volt-VAR Frequency Control: A Smart Building Distributed Model Predictive Control Based Approach

High penetration levels of in Distributed Energy Resources (DERs) as low-inertia renewable energy sources into smart grids imposes imperative Volt-VAR Control (VVC) as well as stability challenges for Distribution Network (DN) operation. Although numerous approaches harness traditional VVC devices to compensate for voltage violations, synthetic inertia and control of energy storage systems exist to improve transient stability with an increase of DERs. While ample strategies tackle these two problems separately, the ability of smart buildings to provide active and reactive power support simultaneously to the grid has not yet been fully exploited. In this work, the effect of the modulation of loads' apparent power consumption on the grid's frequency and voltage profile have been explored. A Distributed Model Predictive Control (DMPC) strategy is presented for voltage and frequency control in the DN provided by smart buildings. The robustness of this strategy is validated on a modified IEEE 13 bus system.
Algorithmic Analysis on Automated Channel Selection Framework for Motor Imagery BCI

Recently, motor imagery (MI) classification in electroencephalography (EEG) based brain-computer interface (BCI) systems is a significant field of brain science study. Since EEG includes multi-channel recordings with massive data, it causes a great challenge for extracting more representative data for proficient classification of MI tasks. Therefore, it is essential to reduce the redundant data from the original EEG signal by electing suitable channels and also to lessen the computation cost. In this work, it is planned to select the most suitable channel using a hybrid model called Geometric Mean based Moth flame hybridized with Firefly Update (GM-MFU) model. Along with this, the suitable samples related to the channels are also chosen via the GM-MFU model. Further, the selected channels and features extracted from the original signal with Common Spatial Pattern (CSP) are classified using Bayesian Linear Discriminate Analysis (BLDA). Eventually, algorithmic analysis is carried out on the proposed GM-MFU algorithm by varying its parameters.
Intelligent Energy Efficiency Algorithm for the 5G Dense Heterogeneous Cellular Networks

Over the past years, telecom network operators have seen an endlessly growing demand for ubiquitous highspeed wireless access and an exceptional increase in connected wireless devices. As a result, we have seen a high growth in traffic volumes. The fifth-generation (5G) heterogeneous cellular networks (HetNets) have been developed by telecom network operators to meet the growing mass data capacity. The 5G dense HetNets is made up of multiple radio base stations (RBSs)/small base stations (SBSs) to increase the coverage and system capacity which led to a high number of network elements. Hence, significantly increases power consumption and lessening the energy efficiency for the telecom network operators. Energy efficiency algorithms have been developed for the 5G dense HetNets, however, these existing energy efficiency algorithms do not satisfy the throughput QoS requirements such as minimalized packet loss, longer battery lifetime, reliability, and high data rates. In addition, real-time traffic types such as voice and video requires high computational load at the terminal side, which have an undesirable impact on energy/battery lifetime which further affects the throughput QoS performance. As a result, this paper proposed an Intelligent Energy Efficiency (IEE) algorithm for throughput QoS and energy efficiency enhancement in 5G dense HetNets. In the proposed IEE algorithm, a deep neural network (DNN) was used to determine the cell capacity ratio for the SBSs. Hence, the SBSs cell capacity ratio was employed as decision criteria to put the SBSs into a sleep state. In addition, transferable payoff coalitional game theory was used in order to ensure real-time applications have a higher priority over non-real time applications. Numerous computer simulation results illustrated that the proposed IEE algorithm experienced an average packet loss of 2.6%, the energy consumption of 3.4 joules, and produced a network throughput of 97.4%.
Ultra-Wideband 4-PAM Backscatter Modulator based on BiCMOS technology for IoT/WPT Applications

This paper presents a Pulse-Amplitude Modulation (PAM) backscatter modulator based on BiCMOS technology chip for Wireless Power Transmission (WPT) applications. Both chip design and measurement setup considered are described. The circuit presents a modulator capable of performing PAM modulation in a wide range of frequencies. Then, to evaluate the communication performance, a low-cost Software Defined Radio (SDR) was used to demodulate the received modulated signals. In a wide range of power levels (-60 to -10 dBm) and different data rates in the chosen frequencies, the circuit performance (FM-band, ISM band, DVB-T band, and GSM band) was demonstrated.
High-Dimensional Similarity Search for Scalable Data Science

Similarity search is a core operation of many critical data science applications, involving massive collections of high-dimensional objects. Similarity search finds objects in a collection close to a given query according to some definition of sameness. Objects can be data series, text, multimedia, graphs, database tables or deep network embeddings. In this tutorial, we revisit the similarity search problem in light of the recent advances in the field and the new big data landscape. We discuss key data science applications that require efficient high-dimensional similarity search, we survey the state-of-the-art high-dimensional similarity search approaches and share surprising insights about their strengths and weaknesses, and we discuss the challenges and open research problems in this area.
A Low-Cost IoT Platform for Heat Stress Monitoring in Dairy Cattle

This paper presents a compact and modular system based on Internet-of- Things for monitoring cattle behavior and stress in real-time. It will help to model certain parameters such as temperature and certain weather variables such as relative humidity, solar radiation, among others thanks to Internet-of- Things (IoT) sensors localized in different points of barns and the fields for cattle farming. A main benefit of the system is that it is built with low-cost hardware and low battery consumption. The wireless system also allows the collection of data in real-time and obtains the temperature-humidity index. This index will give an approach to the heat stress in cattle not only on the farm but in the vicinity of the farm. Finally, the high amount of collected data will allow employing Big Data solutions for estimating the impact on milk productivity. In the future, more sensors will be deployed for a more detailed reading of weather variables and their impact on dairy cattle.
Melanoma Detection Using Convolutional Neural Networks and Group Normalization

Melanoma is one of the most dangerous types of skin cancers. It is very difficult to detect even for experienced dermatologists. Thus, there is a need to develop artificial intelligence-based detection systems to aid in the early diagnosis and treatment of melanoma. The paper proposes a Convolutional Neural Network (CNN) based approach to classify benign and malignant melanoma. The proposed system includes a web application that can be used by the end user. The CNN is trained using a large number of benign and malignant melanoma images. The proposed CNN architecture uses data augmentation and Group Normalization techniques for improved results. The proposed model produces an accuracy of 89.82% with a sensitivity of 92.20% and a specificity of 87.27%. The proposed model is compared to two other experimental architectures and is found to produce the best results. The proposed architecture is also compared to pre-built CNN architectures like ResNet50, MobileNet and InceptionV3 which have been trained on the same dataset. The proposed model outperforms the pre-built architectures and produces appreciably better results.
3D Printing of a Monolithic K/Ka-Band Dual-Circular Polarization Antenna-Feeding Network

In this paper, a Ka/K-band antenna-feeding network in dual-circular polarization is reported. The design of the system was carried out in view of its monolithic manufacturing through selective laser melting in AlSi10Mg alloy. As a proof-of-concept for satellite telecommunication multi-beam applications, the feeding network operates in the K band (19.25, 20.75) GHz and in the Ka band (27.0, 29.0) GHz. The system provides four rectangular-waveguide ports and a common dual-polarized circular-waveguide port to be connected to the feed horn. The prototype exhibits measured values of in-band return loss better than 28 dB and a port-to-port isolation better than 19 dB (in polarization) and 50 dB (in frequency). The cross-polar discrimination is higher than 20 dB. In this regard, an elliptical-waveguide line was specifically designed and manufactured to recover a value higher than 30 dB in both frequency bands. The line can be easily integrated in the feed horn to be connected to the feeding-network thanks the ease of customization provided by 3D printing. The insertion losses are lower than 0.5 and 0.2 dB in the K and Ka bands, respectively. The weight of the prototype is approximately 130 g.
Determination of the Amount of Grain in Silos With Deep Learning Methods Based on Radar Spectrogram Data

Since the grain is a crucial food source, the determination of the quantity of stored grain in silos is inevitable in terms of commercial and correct inventory planning. In this study, a convolutional neural network (CNN) is developed to determine grain quantity using the spectrograms of the radar backscattering data. The radar backscattering signals of different amounts of grain for different grain surface condition types are collected using a stepped frequency continuous-wave radar system. In the scaled model silo, a total of 5681 measurements are carried out for grain stacks with different surface patterns and different weights (0–20 kg). Then, the dataset is constituted by using the spectrograms of these radar measurements. Randomly selected 4261 data corresponding to 75% of the dataset are used for training and the remaining 1420 data are used for testing. The proposed method is compared with pretrained CNN. Accuracy of the methods is given with metric parameters for both classification and regression. The classification task results of the proposed method are obtained as 98.45% accuracy, 98.15% sensitivity, 99.07% specitivity, 98.77% precision, 98.45% F1-Score, and 97.62% Matthews correlation coefficient. The regression task results are calculated as 0.3228 mean absolute error, 0.5150 mean absolute percentage error (MAPE), 0.9649 mean squared error, and 0.9823 root-mean-squared error. The proposed method is also compared with previous studies in the literature (with 3.29 MAPE) and its superiority is demonstrated with metric parameters. The results point out that, if CNN is properly modeled and trained, the combination of CNN and proper signal processing can provide effective results in the quantity measurement applications of the grain stacks.
Fuel Cell Based Inductive Power Transfer System for Supercapacitors Constant Current Charging

Vehicle charging is one of the most discussed topics in the automotive field. Among different technologies, wireless charging systems are becoming a burning issue because of their potentialities related to the advantages introduced in terms of safety and facilities for the final user. In this paper, a focus on the Inductive Power Transfer (IPT) systems is made. In particular, a fuel cell (FC) based one is presented with the aim of supercapacitors (SCs) charging for green transportation applications.
A Highly Efficient and High Degree of Freedom of Position kW-class Wireless Power Transfer System in Seawater for Small AUVs

There are abundant energy and resource minerals in the sea. It is effective for an AUV to move freely in the sea in order to efficiently investigate and utilize these resources, an AUV that moves freely in the sea is effective. The AUV depends on the battery capacity and has a short activity time, so it needs to be pulled out of the sea every time it is charged. In this paper, we examine a magnetic field coupling type wireless power transfer system in the sea to operate AUV efficiently. Using a metal pressure-resistant container such as an AUV, a transfer coil, and a receiving coil, we conduct an experiment of wireless power transfer of high power in salt water. The proposed method is the wireless power transfer system with high degree of freedom in position and transfer efficiency in seawater.