Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simulation Study on the Downlink Performance of Non-Uniform Dense Cellular Networks

The spiraling growth of mobile data traffic has fuelled the development of future-generation cellular networks. Datahungry applications have gained massive popularity owing to the usage of high-tech devices. Aggressively reusing the available spectrum has largely been considered a viable solution to this data deluge. Meanwhile, combating the excessively increasing interference in the networks requires employing efficient interference management schemes. The majority of the available literature focuses primarily on the employment of interference mitigating schemes for randomly deployed network infrastructures. However, owing to the heterogeneity in the network, it is important to investigate the utility of such schemes in spatially dependent deployment scenarios. This paper investigates the impact of fractional frequency reuse incorporation on the downlink performance of spatially dependent cellular networks. Simulations are used to thoroughly evaluate the utility of the adopted approach. It is observed that the utility of the adopted scheme significantly improves the network coverage albeit at the cost of decreased spectrum efficiency.
Flat-Panel Wideband Dual-Circularly Polarized 8×8 Phased Array Antenna for SATCOM Applications

A wideband, dual-circularly polarized (CP) 8×8 phased array is designed for operation at K-/Ka-band, which exhibits wide impedance and axial ratio (AR) bandwidths that encompass both of the desired satellite communication (SATCOM) bands of 22.55–23.55 GHz and 25.5–27.5 GHz. The array has a scan range of ±50° and ±36° at 23.05 GHz and 26.5 GHz, which are the center frequencies of the two aforementioned bands. The array has been integrated with a beamforming network utilizing 5G silicon RFICs, and the measurement results will be presented during the conference.
Detection and Classification of Banana Leaf diseases using Machine Learning and Deep Learning Algorithms

Good yield from banana farms always depend on healthy and disease-free leaves of banana. Hence, it is very essential to detect diseases on time for proper precautions. Manual detection and classification of diseases costs large amount of time and experts' involvement. But implementation of an automated system can help this process within no time. This paper presents three models for banana leaf disease detection and classification using two machine learning approaches, KNN and SVM, and a deep learning approach Alexnet. The Leafspot and Sigatoka are the diseases detected and classified in this work. The RGB colour images are used to train the model to detect and classify the diseased and healthy leaves with and without background. The preprocessed images after data augmentation are used for training the model. The algorithms gave testing accuracies of 76.49%, 84.86% and 96.73% for KNN, SVM and Alexnet respectively.
Structural and optical characterizations of porous anodic alumina–aluminum nanocomposite films on borofloat substrates

Abstract. Structural and optical properties of the porous anodic alumina (PAA)–aluminum (Al) nanocomposite and the PAA-nanostructured films on borofloat substrates are studied. The films are fabricated by the anodization of 170- to 200- and 295- to 330-nm-thick Al sputtered onto the borofloat. The anodization process is stopped at different times in order to form the PAA–Al nanocomposite films with different layer thicknesses. Then, the pore widening is applied to 189- to 210- and 430- to 495-nm-thick PAA films in 5- and 10-min intervals, respectively. The structural properties of the films are characterized by a scanning electron microscopy. The nanocomposite films are also characterized optically by total reflection and directional transmission measurements in the wavelength range between 250 and 800 nm. Our results indicate that controlling the thicknesses of both Al and the PAA layers by anodization time and the morphology of the nanostructures by chemical etching duration in the PAA layer provides unique PAA–Al nanocomposite films with desired optical properties.
Divergence of covariance matrices as a discontinuity attribute for 2D seismic fault detection

Seismic fault interpretation is a crucial step which requires considerable manual intervention by experts. In this work, we aim to develop an efficient fault detection framework. We adopt a multi-stage approach for the task. Initially, we pre-process the seismic data using an iterative anisotropic diffusion filter. Further, patch-based covariance descriptors are computed. A novel discontinuity metric is proposed using exponential-Euclidean Frobenius distance between two adjacent covariance descriptors. The proposed discontinuity metric significantly improves the fault detection performance. Next, Hough transform is applied to extract the possible fault lines, followed by false fault line removal. Finally, optimized fault is detected using support vector regression. The overall efficiency of the fault interpretation framework on both synthetic and real field seismic datasets is discussed in this article.
Heartbeat Classification Using Energy Percentage Distribution with Wavelet Transform and PCA

This work proposes a new feature extraction method to improve heartbeat classification for arrhythmia detection. The method is based on the Energy Percentage Distribution (EPD) obtained with Wavelet Transform (WT) and Wavelet Packet Transform (WPT) of ECG signals. The EPD is obtained by calculating the percentage ratio of the energy of each decomposition level to the total energy of the signal. Using this feature extraction scheme various feature spaces are studied using Principal Component Analysis (PCA) and used to classify five types of beats from the MIT-BIH database with six classifiers: DT, Ensemble, ANN, SVM, kNN, and NB. The results are compared with a traditional feature extraction technique based on Independent Component Analysis (ICA). EPD using WPT in conjunction with PCA generally offered the best performance compared to ICA and EPD with WT.
Automated Detection of Common Maternal Fetal Ultrasound Planes Using Deep Feature Fusion

Ultrasound is the primary imaging modality used to assess the development and well-being of the fetus during pregnancy. Identifying the right anatomical structure plays an important role to monitor the fetus development. However, identification of the right anatomical structure is a difficult and time-consuming process even for the skilled sonographer. Therefore, a deep learning-based automated detection system of common maternal fetal ultrasound planes using deep feature fusion is proposed. The deep attributes extracted from pretrained ResNet-50 and VGG-19-GAP are fused. These fused deep feature descriptors are given to the multiclass support vector machine to classify the fetal ultrasound planes into six classes such as the abdomen, brain, femur, thorax, cervix, and other planes. Experimental outcomes indicate that the developed multiclass categorization of fetal ultrasound planes using deep feature fusion outperforms existing state-of-the-art approaches in terms of accuracy.
Machine Learning for Rectangular Waveguide Mode-Identification, Using 2D Modal Field Patterns

We apply machine learning (ML) techniques to identify the modes in rectangular waveguides from images of 2D modal field patterns injected with uniform, exponential, correlated exponential, and Gaussian noise distributions. A binary classifier is used to identify either transverse electric (TE) or transverse magnetic (TM) modes, and a Multi-class classifier is used to identify the mode numbers. Signal to noise ratios of 1, 0.1, and 0.01 are used to show the effectiveness of each model. Results show accuracy scores up to 99.95%. Several examples demonstrate that noisy modal patterns (unidentifiable to human eyes) may be successfully classified by the ML model.
Single-Stage Extendable Multi Level Inverter with Integration of Step-Up Switched-Capacitor Unit & DC-Link Capacitor Unit

The basic configuration of H-Bridge multilevel inverter (HBMLI) is capable to produce desired number of voltage levels but suffers due to higher number of switches and thus deals with the higher complexity and cost. In this paper a new inverter configuration is proposed which is capable to produce 13/17 voltage level. The proposed inverter comprises of three modules namely Modified H-Bridge, DC link Capacitor unit and Step-up capacitor unit. This is a single stage extendable multilevel inverter and the voltage level of inverter will increase if number of step-up switched-capacitor unit and DC-link capacitors is increased. Step-up switched-capacitor/DC-link capacitor unit charged by using the both the binary asymmetrical algorithm and binary symmetrical algorithm. The proposed inverter configuration is simulated for 17 Level and the results has been verified using MATLAB/SIMULINK software.
Power Efficient Approximate Booth Multipliers for Error Resilient Applications

This paper proposes new approximate Booth multipliers with an aim to reduce the power and power-delay product with acceptable accuracy. The proposed multiplier architecture is segmented into two regions: The approximate region, and the accurate region (most significant region). In the approximate region, partial products are reduced using four new approximate compressors of varying degrees of simplification to reduce error, while exact compressors are used in the accurate region. For the three proposed 16-bit multiplier designs, synthesis results show an improvement of 28.98%, 21.15% and 20.41% in power-delay-product respectively, when compared with existing state-of-art existing designs,, without compromising on the accuracy. Finally, proposed designs are validated using image processing applications.
An Objective Pain Measurement Machine Learning Model through Facial Expressions and Physiological Signals

The widely used technique for measuring patient pain in modern medicine is based on patients' self-reported pain. Since each human body experiences pain differently and has its own distinct pain threshold, it is very difficult for professionals to objectively evaluate a patient's level of pain based on their own description. In order for pain management treatments and prescribed therapy to be efficient and effective, a precise evaluation of pain is crucial. This study developed an objective pain measurement model based on machine learning algorithms and an open-source BioVid heat pain database. More specifically, the properties of skin conductance at various pain thresholds were examined. Electrocardiogram (ECG) and facial expression data were also assessed for performance. The study's findings demonstrate that both facial expression-based and ECG-based models can detect whether a patient is in pain. The physiological response of the subject can be utilized as a nonverbal test to gauge the patient's level of discomfort. The ECG-based model is more precise than the one based on facial expressions because we are able to gather more data with the former, therefore improving the model's accuracy, by increasing the period of the pain stimulation and the number of participants.
Lubricated pipelining: stability of core–annular flow. Part 4. Ginzburg–Landau equations

Nonlinear stability of core-annular flow near points of the neutral curves at which perfect core-annular flow loses stability is studied using Ginzburg-Landau equations. Most of the core-annular flows are always unstable. Therefore the set of core-annular flows having critical Reynolds numbers is small, so that the set of flows for which our analysis applies is small. An efficient and accurate algorithm for computing all the coefficients of the Ginzburg-Landau equation is implemented. The nonlinear flows seen in the experiments do not appear to be modulations of monochromatic waves, and we see no evidence for soliton-like structures. We explore the bifurcation structure of finite-amplitude monochromatic waves at criticality. The bifurcation theory is consistent with observations in some of the flow cases to which it applies and is not inconsistent in the other cases.
Implementation of convolution layer in FPGA for disease classification in Tomato leaves

Agriculture is the cornerstone of the country’s income and is essential to fulfill the demands of a growing population. Plant diseases are an intimidation to the nation’s food security as they reduce the yield and quality of food grains and fruits. Early discovery of these diseases can be done through Artificial Intelligence (AI). Convolutional Neural Networks (CNN) is one of the promising techniques for the accurate classification of diseased plants from images. This paper classifies the tomato leaves as diseased or healthy using CNN models. The plant village dataset is used for classification. The classification accuracies of two different CNN models, named CNN_1 and CNN_2 is compared and found that CNN_1 has better performance. The convolution layer which is the backbone of all the CNN models is implemented in FPGA. The data movements between the different layers are one of the crucial constraints, which results in excess time and consumes more power while implementing the CNN accelerator in hardware. Two dataflow architectures, No Local Reuse and Weights Stationary are used in this paper to manage data movements and the results are compared.
Coding Mel Spectrogram using Keras and Tensorflow for Home Appliances Tiny Classification

Mel power spectrogram has been extensively used as audio pre-processing for both feature extraction and transformation. Between many, one of the most used libraries is Librosa. In this paper, we prove that the Mel power spectrogram processing algorithm can be coded using Keras and Tensorflow software primitives featuring several statically initialized or trainable hyper-parameters. That approach would allow to achieve a self-contained end to end deep learning framework dependent implementation not possible with other software libraries. Moreover, it can be either initiable with pre-computed parameters or let them be trainable as part of the same multitask model as any subsequent machine learning approach. The pipeline has been tested on COOLL, WHITED and PLAID datasets for electric current classification of home appliances. The difference in accuracy was +3.28% on COOLL, + 1.56% on WHITED and −0.16% on PLAID with respect to reference work from same authors. The complexity of the proposed approach has been estimated in term of multiplied and accumulated operations. It resulted be 48,821 MACC/s for an estimated number of cycles/s of 439,389 for STM32L4 and 292,926 for STM32H7.
Detection of COVID-19 in High Resolution Computed Tomography Using Vision Transformer

In the current pandemic, precise and early diagnose of COVID-19 patient remained a crucial task for control of the spread of the COVID-19 virus in the healthcare sector. Due to the unexpected spike in COVID-19 cases, the majority of countries have experienced scarcity and poor testing rate. Chest X-rays and CT scans have been discussed in the literature as a viable source of testing for COVID-19 disease in patients. However, manually reviewing the CT and x-ray images is time-consuming and prone to error. Taking account into these constraints and the improvements in data science, this research proposed a Vision Transformer-based deep learning pipeline for COVID-19 diagnose from CT-based imaging. Due to the scarcity of large data sets, three open-source datasets of CT scans are pooled to generate 27370 images of covid and non- covid individuals. The proposed vision transformer-based model accurately diagnoses COVID-19 from normal chest CT images with an accuracy of 98 percent. This research would assist the practitioner, radiologist and doctors in early and accurate diagnose of COVID-19.
The Excitation Temperatures of HC9N and Other Long Cyanopolyynes in TMC-1

We report observations of seven different transitions of HC9N between 9.8 and 23.3 GHz in the cold, dark cloud TMC-1. Because of its lower rotational constant, HC9N is expected to yield a rotational temperature closer to the kinetic temperature of the gas than the shorter cyanopolyynes. Assuming that HC9N in TMC-1 is optically thin, we obtain a rotational temperature Trot = 7.5-8.7 K and a corresponding column density of NL(HC9N) = (5.4-2.3) × 1012 cm-2, depending on the source size L assumed. The rate of radiative cooling is less efficient in the longer cyanopolyyne chains and is approximately proportional to (n + 1)-6, where n is the number of carbon atoms in the chain. In steady state this is balanced by the collisional rate of excitation. The rotational temperatures of the cyanopolyynes are found to increase with the number of heavy atoms over the range HC5N-HC9N, in reasonable agreement with calculations. Concomitantly, we find evidence that the longer cyanopolyynes are located in regions that become progressively smaller with chain length. We also report newly measured values for the rotational and centrifugal distortion constants of HC9N that improve the accuracy of the calculated millimeter wave transitions.
A Real-time Predictive Study on the Deformation of Incremental Launching Construction of Steel Box Girder Based on Numerical Inverse

In order to obtain a finite element model of steel box girder incremental launching construction that matches the actual structure and to solve the problem of difficult prediction of structural deformation in incremental launching construction, this paper combines the incremental launching construction with the response surface finite element correction technique and proposes a real-time prediction method of steel box girder incremental launching construction deformation based on numerical inversion. The method is applied to the calculation of a steel box girder step incremental launching construction. Firstly, the initial incremental launching construction full working condition finite element model is established, the modulus of elasticity and density of the main girder, guide girder and link truss of the jacked bridge are selected as the parameters to be corrected, the test design is carried out using the central composite design method, and the test sample calculation is quickly carried out in combination with the Python interface in the secondary development interface of ABAQUS software to obtain the response The objective function is constructed by using the Fmincon solver in Matlab to solve for the objective function, and the corrected parameters are substituted into the original model to calculate the structural deformation for the subsequent working conditions. The model is then modified if it exceeds the warning value, and recorded if it does not. The results of the study show that after the four finite element corrections, the calculated values of the subsequent predicted working conditions are very close to the measured results, indicating a good correction effect. Compared with the initial finite element model, the error is significantly reduced, and the error between the corrected response values and the measured values is less than 10%.
UDDSUI: An Unsafe Driving Detection System Using IoT

People’s increased use of automobiles has resulted in terrible events such as traffic accidents, deaths, and financial losses. The main elements associated with the most mishaps are driver errors and human variables influenced by medical conditions, alcohol intake, reckless or dangerous driving etc. Driver fatigue is seen as another key factor in automobile accidents. The majority of vehicular accidents are caused by the driver’s actions. Data about the driver, the weather, and the vehicle may be used to determine unusual events on the road. In this paper, we offer a unique and effective method for preventing disasters caused by a driver’s actions. ’Unsafe Driving Detection System Using IoT’ (UDDSUI) is the name given to the proposed system. The UDDSUI system monitors the proximity of an adjacent car, alcohol intake and the exhaustion of the driver. It sends a warning message to the driver and stores the data in a cloud service via the Internet of things (IoT).
Multimodal Feature Selection for Detecting Mothers' Depression in Dyadic Interactions with their Adolescent Offspring

Depression is the most common psychological disorder, a leading cause of disability world-wide, and a major contributor to inter-generational transmission of psychopathol-ogy within families. To contribute to our understanding of depression within families and to inform modality selection and feature reduction, it is critical to identify interpretable features in developmentally appropriate contexts. Mothers with and without depression were studied. Depression was defined as history of treatment for depression and elevations in current or recent symptoms. We explored two multimodal feature selection strategies in dyadic interaction tasks of mothers with their adolescent children for depression detection. Modalities included face and head dynamics, facial action units, speech-related behavior, and verbal features. The initial feature space was vast and inter-correlated (collinear). To reduce dimension-ality and gain insight into the relative contribution of each modality and feature, we explored feature selection strategies using Variance Inflation Factor (VIF) and Shapley values. On an average collinearity correction through VIF resulted in about 4 times feature reduction across unimodal and multimodal features. Collinearity correction was also found to be an optimal intermediate step prior to Shapley analysis. Shapley feature selection following VIF yielded best performance. The top 15 features obtained through Shapley achieved 78 % accuracy. The most informative features came from all four modalities sampled, which supports the importance of multimodal feature selection.
High Precision Sound Event Detection based on Transfer Learning using Transposed Convolutions and Feature Pyramid Network

We introduce two models for high precision sound event detection leveraging transfer learning. The sound events we detect include “speech”, “music”, and “chime”. Both models consist of a CNN backbone pre-trained using AudioSet for audio classification. To get high precision detection results, the first model employs transposed convolutional layers as the detection head, while the second model uses Feature Pyramid Network(FPN) as the detection head. Experimental results show 98.8% accuracy and 98.6% F1 score on a private test set, from the one using FPN. Both models outperform a two-stage model using LSTM, various model ensembles, and a pre-trained neural network model for audio classification.
Design and Analysis of Metamaterial Inspired Electrically Small Circularly Polarised Rectangular Patch Antenna

An electrically small metamaterial inspired circularly polarised rectangular patch antenna is presented through this paper. The antenna includes a double negative complementary metamaterial structure loaded on a patch antenna. This structure is capable of producing a miniaturisation of 58.6% with respect to the conventional patch antenna. The microstrip rectangular patch antenna before loading, resonates at 5.8 GHz, and metamaterial loading enables its resonant frequency tuning upto 2.14 GHz. The configuration offers circular polarisation. The prototype of the antenna is fabricated on FR4 substrate for the WLAN application band of 2.4 GHz. The proposed antenna has the electrical parameters, ka = 0.964, Qchu =1.6 and Qrad =48.3. The proposed antenna is invariably electrically small with the values of ka and Qrad satisfying Chu limit (ka< 1 and Qrad >10). The loaded metamaterial structure causes this electrical smallness and polarisation change. The electrical dimension of this compact antenna is 0. 24λ0 × 0. 192λ0 × 0.013 λ0 and physical dimension is 30 × 24 × 1.6 mm3, with impedance bandwidth 2.07% and gain 3.3 dBi at 2.4 GHz.