Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Machine Learning Approaches for Predicting the Progression of Crohn’s Disease

The incidence of Crohn’s disease (CD) is rising, which calls for more accurate and less invasive diagnostic tools. The concentration of Faecal Calprotectin (FC) is a reliable indicator of luminal inflammatory processes and can replace invasive and uncomfortable ileocolonoscopies. Studies have confirmed the association of FC levels with the progression of CD and various machine learning approaches have been used for predicting disease progression. In this study, we aimed to comparatively evaluate the performance of established machine learning approaches, to predict the progression of CD, using a range of variables, including FC levels. Our dataset consisted of records for 804 patients with CD and a FC measurement, from a teaching hospital that cares for secondary and tertiary referred patients. We compared the performance of four machine learning approaches, namely logistic regression, support vector machine, random forests and artificial neural networks, to predict the likelihood of a flare up. Our results showed that all four approaches performed strongly, which demonstrates the potential of these approaches, in particular logistic regression, for predicting disease progression. Logistic regression slightly outperformed the others, with an accuracy of 0.90 and an AUC of 0.83. Our dataset had missing data for a number of patients, which resulted in fewer variables being selected for inclusion in the model. Our relatively small sample size could account for SVM, Random Forest and the ANN not demonstrating superior accuracy compared to logistic regression, in this study. In future, an increased number of variables should be included for analysis, the outcome period for a flare up should be explored, and our results should be validated using another independent and large dataset.
Broadband Highly Efficient Doherty Power Amplifiers

Modern wireless communication systems with large bandwidth and high peak-to-average power ratios (PAPRs) have been and will continue to be in high demand. This puts a strain on the transmitter, which has to operate with its average power in the output back-off (OBO) region. The Doherty power amplifier (Doherty PA) was invented in 1936 as an alternative to the highly inefficient linear amplifiers used in radio transmitters. The Doherty PA has recently undergone a renaissance, becoming the PA architecture of choice for mobile radio base stations. The additional efficiency peak at the OBO point makes it ideal for high PAPR signals. However, the main drawback of conventional Doherty PAs is its narrow bandwidth, which significantly limits its applicability. Recently, broadband Doherty PAs have been realized using a technique called "post-matching" and have attracted much attention. In this paper, the operating principles of conventional and broadband Doherty PAs are reviewed, followed by an analysis of recently reported prototypes, current technical challenges, and application in 5G wireless communication systems. As a comparison, process technologies for the implementation of Doherty PAs are also surveyed.
Design Issues and Challenges of an FPGA-based Orthogonal Matching Pursuit Implementation for Compressive Sensing Reconstruction

Compressive sensing (CS) is as an evolving research area in signal processing due to the advantages offered for signal compression. Based on the sparsity of signals, CS allows the sampling of sparse signals under the sub-Nyquist rate, and yet promises a reliable data recovery. To date, the implementation of practical applications of CS in hardware platforms, especially in real-time applications, still faces challenging issues due to the high computational complexity of its algorithms, hence leading to high power-consuming processes. There are several CS reconstruction approaches, and orthogonal matching pursuit (OMP) is one of the best and popular algorithms implemented. However, this algorithm faces two (2) major process issues: optimisation and the least square problem. Due to OMP’s significant contribution, this paper presents an overview of the design issues and challenges of OMP algorithm implementation for CS reconstruction. The field-programmable gate array (FPGA) as a viable hardware solution for OMP implementation is reviewed and discussed based on reconstruction time, signal size, number of measurements, sparsity and features.
Spatial Data Management in IoT systems: A study of available storage and indexing solutions

As the Internet of Things (IoT) systems gain in popularity, an increasing number of Big Data sources are available. Ranging from small sensor networks designed for household use to large fully automated industrial environments, the Internet of Things systems create billions of measurements each second making traditional storage and indexing solutions obsolete. While research around Big Data has focused on scalable solutions that can support the datasets produced by these systems, the focus has been mainly on managing the volume and velocity of these data, rather than providing efficient solutions for their retrieval and analysis. A key characteristic of these data, which is, more often than not, overlooked, is the spatial information that can be used to integrate data from multiple sources and conduct multidimensional analysis of the collected information. We present here the solutions currently available for the storage and indexing of spatial datasets produced by the IoT systems and we discuss their applicability in real world scenarios.
Ring artifacts segmentation on microtomographic images by convolutional neural networks

Ring artifacts in X-ray microtomographic images can lead to errors in the construction of digital twins of rock samples for flow simulation. Previously, we considered an algorithm for detecting ring artifacts by means of matching filtering of image slices in a polar coordinate system. However, that approach is inapplicable for an arbitrary fragment of an image and requires adjustment of parameters from image to image. In this paper, we propose the segmentation method based on convolutional neural network. Two network architectures are considered: SegNet and U-net. To create a big and representative training and validation datasets, we propose an algorithm for transferring ring artifacts detected by the existing approach from one image to another. Our task-specific data augmentation improves outcomes in comparison with conventional augmentation techniques. The trained model successfully segments ring artifacts even for sample images and artifacts that were not in the training set. The developed algorithm is used to assess the quality of microtomographic images and local correction of image regions damaged by ring artifacts.
Independent Component Analysis Based on Natural Gradient Algorithm for Text Mining

The volume of texts produced daily is growing significantly. Computers have difficulty processing and interpreting this considerable amount of largely unstructured data without using feasible techniques. Efficient and effective techniques and algorithms are important in exploring useful patterns. Text mining (TM), the process of extracting useful knowledge from text, is gaining considerable research attention. In this article, we explain the natural gradient independent component analysis (NGICA) of text classification (TC), which is an application of TM involving the preprocessing of text data followed by classification. An unsupervised framework of machine-learning classification is proposed based on NGICA for TC. A manually labelled dataset is used to assess the proposed model, and its validity is proven through experiments. The proposed model achieves an overall F-measure of 83% for TC. Results show the potential of the proposed model for Compared with the infomax method that provided 81% of an overall F-measure for TC.
Learning congestion over millimeter-wave channels

This paper studies how learning techniques can be used by the congestion control algorithms employed by transport protocols over 5G wireless channels, in particular millimeter waves. We show how metrics measured at the transport layer might be valuable to ascertain the congestion level. In situations characterized by a high correlation between such parameters and the actual congestion, it is observed that the performance of unsupervised learning methods is comparable to supervised learning approaches. Exploiting the ns-3 platform to perform an in-depth, realistic assessment, allows us to study the impact of various layers of the protocol stack. We also consider different scheduling policies to discriminate whether the allocation of radio resources impacts the performance of the proposed scheme.
Structure of Polypropylene-based Nanocomposites containing Calcium Zirconate

Polypropylene (PP) has recently been proposed as a good alternative to cross-linked polyethylene (XLPE) in the field of dielectrics due to PP’s beneficial properties to withstand higher thermal endurance of up to 150°C along with its ability to be recycled with ease, when compared with XLPE. However, PP is much stiffer than XLPE, making it unsuitable to be extruded as a high voltage cable insulation. Furthermore, PP has poor thermal conductivity under room temperature when compared with XLPE, which will otherwise result in inferior dielectric performances. Therefore, PP needs to be modified to alter its physical as well as electrical properties. In the current work, ethylene-propylene-diene monomer (EPDM) was proposed to be combined with PP to produce a PP blend with reduced overall stiffness. To increase the thermal conductivity of the PP blend, nanofillers were proposed to be added to the PP blend. For these reasons, the structure of the proposed materials was investigated by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC).
Designing Feature Application for User Experience to Censor Inappropriate Scene in Indonesia

The objective of this article is to build a better and more realistic user interface for Vlarm, an application we developed to censor inappropriate scenes during the COVID-19 pandemic. This premise was built by the fact that most children demand home entertainment, such as watching movies during the quarantine. Thus, this research introduces a new feature in our censorship software that enables parents to exchange their censorship findings so that other parents can use such findings without doing so to the same movie. On the other hand, the Indonesian Government imposes a large-scale social restriction (PSBB) due to the situation of COVID-19 in the country. This interaction is to provide information on the user's experience to eventually build more user-friendly interfaces and features that enable users to conveniently utilize Vlarm application.
Warm-Start Expedited Optimization of Antenna Structures Using Kriging Surrogates and Iterative Correction Scheme

Electromagnetic (EM)-driven adjustment of geometry parameters, often referred to as design closure, is nowadays a mandatory stage of antenna design process. It is executed to improve the system performance as much as possible while taking into account parameter dependencies that cannot be handled by simpler methods (e.g., theoretical models), typically employed at the initial steps of the antenna development. Unfortunately, EM-based optimization is a time consuming task, especially when the quality of the initial design is poor. This paper proposes a novel framework for accelerated antenna optimization. Our methodology involves a database of previously obtained designs, kriging interpolation models, and an iterative correction scheme for design refinement. The incorporation of pre-existing data permits generation of a good initial design as well as rapid optimization entailing the cost of just a few EM antenna analyses. The technique is demonstrated using a dual-band uniplanar dipole antenna. The structure is optimized within wide ranges of operating frequencies (2 GHz to 3 GHz for the lower band and 4 GHz to 5.5 GHz for the upper band). Benchmarking indicates that the proposed approach exhibits improved computational efficiency and reliability as compared to gradient-based warm-start optimization techniques.
Development of algorithms for annotating information in social networks

The work is dedicated to the research of information annotation algorithms in social networks. The social network Instagram was selected as the object of the research. Studies in the field the data collection of the social network Instagram were conducted to solve the problem of obtaining the necessary information. Software tools that provide the collection of necessary data and the annotation of the information have been developed. The existing algorithms for annotating information have been investigated and applied on images as the main data stored by the social network Instagram. The results of the program complex work were analyzed and features of its work were also identified.
Dual-Band Radar System with Multiple Distributed Sensors for Coherent MIMO

A new architecture for a widely distributed dual- band coherent multiple-input multiple-output (MIMO) radar system is illustrated, and its implementation and testing are reported. The system consists in a central unit based on a single mode-locked laser, supporting multiple remote radar nodes linked by standard optical fiber pairs. Every remote node operates both in the S- and X-band, and it can be displaced over distances of several kilometers, allowing to monitor a scene under different angles of view. All the remote nodes share the same oscillator and digital signal processing unit, both located in the central node. This architecture allows to perform centralized data fusion on the signals acquired by the remote nodes, and, by virtue of the system coherence, to take advantage of the coherent MIMO processing strategy thus to offer a superior spatial resolution, which is even magnified by the dual-band approach.
Outdoor Propagation Channel Investigation at 26 GHz for 5G mmWave Communication

The millimeter wave (mmWave) frequency spectrum is being considered as the most promising communication systems to fulfill the bandwidth requirements for the next-generation wireless network. However, the mmWave frequency band has encountered several issues like fading, scattering, atmospheric absorption, and penetration losses as compared to the currently using frequency spectrum, which is under 6 GHz. Therefore, optimizing the propagation channel path at the mmWave band is seemed to be the only solution, which can help the operators to distinguish the signal behavior before practically implement the 5G networks. In this regard, this study aims to analyze the propagation characteristics for 26 GHz frequency band for the outdoor Single Side Parking (SSP) and Double Side Parking (DSP) environment. It utilizes the most potential propagation path loss models i.e., Close-In (CI) and Floating Intercept (FI). The results prove that the CI model can produce better results and delivers higher network performance as compared to the FI model.
TLP-IDS: A Two-layer Intrusion Detection System for Integrated Electronic Systems

With the increasing applications of integrated electronic systems (IESs), especially in security critical application scenarios like satellites and aircraft, new vulnerabilities and attacks have emerged recently. To detect the attacks, we propose TLP-IDS, a real-time intrusion detection system (IDS). TLP-IDS includes two layers of detection modules, one based on time and sequence logic and the other based on historical data. For the modules in the first layer, periodic and aperiodic messages are distinguished based on variations of message intervals, and we learnd from the idea of Markov decision process (MDP) in reinforcement learning (RL) to automatically learn the logical relationship between sequences. In the second layer, an online sequence extreme learning machine (OS-ELM) method is deployed to fit the data and further combined with the Weibull distribution function for prediction and detection. To evaluate our system, we implement several attack scenarios on a test bed, and measure the detection performance. Experimental results show that our system can quickly and effectively detect various attacks.
Enabling I/O-Efficient Redundancy Transitioning in Erasure-Coded KV Stores via Elastic Reed-Solomon Codes

Modern key-value (KV) stores increasingly adopt erasure coding to reliably store data. To adapt to the changing demands on access performance and reliability requirements, KV stores perform redundancy transitioning by tuning the redundancy schemes with different coding parameters. However, redundancy transitioning incurs extensive I/Os, which impair the performance of KV stores. We propose a new family of erasure codes, called Elastic Reed-Solomon (ERS) codes, whose primary goal is to mitigate I/Os in redundancy transitioning. ERS codes eliminate data block relocation, while limiting I/Os for parity block updates via the new co-design of encoding matrix construction and data placement. We realize ERS codes as a KV store atop Memcached, and show via LAN testbed experiments that ERS codes significantly reduce the latency of redundancy transitioning compared to state-of-the-arts.
Influence of the receptive field size on accuracy and performance of a convolutional neural network

Convolutional neural networks (CNNs) have been successfully applied to many tasks such as digit and object recognition. In this paper we study the size of the receptive field of deep convolutional neural networks, in particular, we check the idea of a "redundant" receptive field. We run a set of experiments on two common CNN models — VGG16 and ResNet18 — in order to explore the influence of receptive field size on CNN's training time, accuracy, and performance. We run experiments using the MakiFlow framework on the CALTECH256 dataset. The experiments' results show that the optimization of neural networks (NNs) by reducing the size of the receptive field allows to reduce the NN’s training time by 5-7% while maintaining the accuracy of the network.
Spatiotemporal Spectral Analysis of Signals and Active Interference in Radar with Digital Antenna Arrays

The work considers the formation of a spatiotemporal signal at the output of a linear antenna array and the characteristics of this signal in the frequency domain. The concept of signal broadband in the spatiotemporal sense is discussed and its effect on signal detection against a background of active interference is considered. Based on the study of the spatiotemporal spectral characteristics of the signal and active interference in the radar system, an approach to signal processing based on the spatiotemporal spectral analysis is proposed.
Analysis of the Impact of Power Transformer Loading on the Transformer Oil Aging Intensity

Operating temperature, which depends on the load of transformers, was analysed as a factor affecting the intensity of aging oils. The analysis results provided a quantitative assessment of the impact of transformer load on the drift intensity of oil indicators. In particular, the dependencies of the induction period duration (time during which the values of indicators do not change) and the values of oil indicators increments after 25 years of operation on the average transformer load for a given period were obtained. It has been established that formation of water-soluble acids (the most dangerous oxidation products) in oil occurs only in transformers whose average load has exceeded 50% during the last 13 years. Increase of transformer load from 10 to 50% leads to 3.5 times increase of organic acids content after 25 years of operation (from 0.029 to 0.102 mg KOH per 1 $g$ of oil). The obtained results allow using more advanced methods of transformer oils condition assessment (for example, trajectory method or methods based on regression models), but also forecasting their values.
Investigation of Structural Parameter Variation on Extended Gate TFET for Bio-Sensor Applications

Traditional Gate engineered Metal Oxide Semiconductor (MOS) technology faced serious challenges in terms of greater sensitivity for target biomolecules and to be utilized as the state-of-the-art Nano-recognition tool. Research on a tunnel field-effect transistor (TFET) started with the aim to achieve fast detection, low power consumption, and its potential for on-chip integration capability. Dielectric Modulated TFET (DMTFET) has established itself to be a primary candidate for sensing both charged and charge-neutral species with volumetric sensitivity. As extended gate DMTFET happens to be inferior to its short gate counterpart, we have devised ways to achieve superior performance only by making variations over structural electrostatics. With the incorporation of most possible ways of modulation, we present two orders of magnitude on-current increment and a considerable percentage of sensitivity improvement over the conventional one. Future scopes having noteworthy diversifications have also been analyzed with proper justification.
Eye Diseases Classification Using Hierarchical MultiLabel Artificial Neural Network

several types of research adapted to classify or diagnosis eye diseases using different classification techniques with different feature extraction techniques as well as with different eye data sets. The goal of this paper is that utilizing dataset of images of digital camera to classify external eye diseases. This dataset is categorized into four major classes depends on the part of the eye where the disease causes the problem. These classes were further subdivided into several subclasses equal to the number of diseases in that class. Colour histogram features utilized from Hue, ${\rm Min}, {\rm Max}$, Diff (HMMD) colour space were quantized non-uniformly as defined in MPEG-7 standard into 32-bins in addition to Laws texture features utilized from Sum channel of HMMD colour space extracted to be classified. The contribution of this paper is that a new classification technique which is Hierarchical Multi-label Classification (HMC) is applied. The overall prediction accuracy achieved is % 75.7142.
Fundus Image Segmentation Using Decision Trees

In this work, the study on feature selection effectiveness using decision trees was conducted. Our previous work allowed us to identify the most informative texture characteristics using the selection traits technology. The technique made it possible to carry out intelligent analysis of features using color subspaces when solving the problem of selecting regions of interest. In this paper, decision trees were constructed using texture features that were used for the previously proposed technology. Decision trees selected new informative signs, providing higher accuracy. The most effective number of features is 3 when the window size is more than 15. For windows of dimension 12, decision trees using more than 6 texture features provided accuracy above 98.
A Mach-Zehnder Modulator Quadrature Point Biasing Circuit Immune to Laser Power Fluctuations

We present a low power, reference-less digital CMOS control loop for Mach-Zehnder Modulator quadrature biasing, which is insusceptible to laser power fluctuation. The design has been simulated in a 65 nm CMOS process, and a Verilog-A code was developed to model the SOI photonic circuit.
Modelling V2V message generation rates in a highway environment

In this paper, we investigate if an analytical model can be used to estimate the load on the vehicular communication channel. We design a queueing model for estimating the probability distribution of Cooperative Awareness Message generation rates in a highway environment. The results are compared with a real world vehicular traffic trace on a highway and also with a vehicular communication simulator using Veins and SUMO for more complicated traffic scenarios involving acceleration and deceleration ramps. Comparing the results shows the probability distribution of the message generation rates predicted by our model to be within the 95% confidence interval of the distribution obtained from the traffic trace and the simulation.