Miracle-dz/UniFAR-FTUs huggingface dataset:
Piano Notes Recognition using Digital Signal Processing

Extraction of notes of a song is crucial for varied intentions such as teaching, listening, studying, and composing. The paper entails the extraction of the notes of a given piano song. The fundamental frequencies of each sound note are found and then mapped to the corresponding notes. The research is carried out firstly using various piano songs with pre-identified notes. Then, a comparison is done between the identified notes and the originally known notes; till maximum accuracy is achieved. When the detection rate increases, the detection algorithm is then implemented for songs having unknown notes. The methodology used for the extraction is Fast Fourier Transform(FFT) and the accuracy achieved is 99.8%. The note recognition app is available these days but, with high charges and payment; hence, this work might be a contribution to the making of free software for note detection which is easy to use and can be freely available.
Hybrid Transfer Learning with ECOC Ensemble Configuration for COVID-19 CXR Detection

Coronavirus Disease 2019 (COVID-19) is a viral pneumonia that causes symptoms in the lungs of those infected. The presence of the symptoms must be diagnosed as soon as possible. If no test kits are available, the next best alternative is a computer-aided diagnostic of a patient’s chest X-ray scan for a quick and accurate diagnosis. This paper proposes a hybrid transfer learning method with Error-Correction Output Codes (ECOC) by combining networks including GoogLeNet, ResNet-18, and ShuffleNet for feature extraction. X-ray input data are collected from open-source repositories. In this implementations, Support Vector Machine (SVM) as the base classifier. The proposed network attempts to categorize the input data into one of three categories: COVID-19, healthy, and non-COVID-19 pneumonia. The mean accuracy of our method is 96.21%, compared fine tuning existing pre-trained model which yielded 89.1% for GoogLeNet, 88.95% for ResNet-18, and 89.31% for ShuffleNet.
A Transformer Tap Changer and Vector Group Study for Load Flow Analysis in Brunei Network

Load flow analysis plays an important role to forecast power system operation planning, economic scheduling, and reconfiguration of power network. In this paper, a study for load flow analysis in a typical power network in Brunei Darussalam is presented. The modelling and simulation of the actual power network is employed in the MATLAB with modified the scripts of the - 30 bus system IEEE script and compared with power system simulator engineering (PSSE/34) software. In addition, a comparison of load flow analysis involving with and without transformer tap changer and vector group is also presented in this paper. It was found that the load flow results were improved by employing the transformer tap changing and their phase-shift vector groups into the network.
Deep Neural Network-Based Human Activity Classifier in 60 GHz WLAN Channels

In 6G systems, close integration of communication and sensing is envisaged. Human monitoring and in-air gesture recognition having a wide range of applications in smart homes, emergency systems or games, represents a typical sensing task where the monitoring of received communication signals has the potential to substitute currently used video cameras or dedicated sensors. Although there is still a long path until the real deployment of 6G will become available, it is already possible to test the main ideas of integrated sensing and communications within the current standards. The paper addresses Deep Learning based estimation of several typical people’s activity from WiFi-like signals in 60 GHz channel, that can be deployed in future smart access points. As shown in previous works, each activity generates a unique Doppler pattern that is extracted from the received signal. As the Convolutional Neural Network was used for classification, this paper also describes the method for obtaining a sufficient number of training samples for each activity. We also try to investigate classifier performance deterioration by varying distance of person from test-bed antennas and by testing on persons that have not participated during the classifier training.
Assessment of Deep Neural Network and Gradient Boosting Machines for Credit Risk Prediction Accuracy

For financial services to be competitive, artificial intelligence and machine learning have become essential. This study benchmarked Deep Neural Networks (DNN) and Gradient Boosting Machines (GBM) based on their superior model accuracy performance in most previous studies. Consequently, this study aims to perform a comparative analysis of classification power between DNN and GBM. To date, there is no such comparison. Each model underwent extensive tuning to achieve performance appropriate for the comparison in this study. The author deployed three renowned credit risk datasets for model construction to train GBM, DNN + ReLU, DNN + Maxout, and DNN + Tanh classifiers. For the model evaluation, the area under the receiver operating characteristics curve (AUC ROC) was utilized to assess the models' accuracy performance. The assessment also includes investigating the effect of splitting the training/test sets using the split method of 80:20 split and 70:30 split. The results indicate that GBM tends to be faster and more potent than DNN due to its lower processing requirements. GBM is hence the preferred method for credit scoring prediction. Overall, this study observed that DNN and GBM could be the cutting-edge for solving classification problems. Nevertheless, GBM should be the first-choice option due to its simplicity, quicker to train, and higher accuracy.
The source of the threading dislocation in GaSb/GaAs hetero-structures and their propagation mechanism

The misfit and threading dislocations during GaSb epitaxy on GaAs substrate were investigated by weak beam dark field and high angle dark field scanning transmission electron microscopy. The geometric phase analysis and dislocation density tensor analysis were next used to analyze the strain around the threading dislocations and quantify the corresponding Burgers vectors. It is shown that there are three types of threading dislocations (mixed, edge, and pair of mixed types) close to the interface, which originate from the 60°, Lomer, and 60° pair misfit dislocations, respectively. During the growth of the epitaxial layer, the edge type as well as the pair of mixed threading dislocations split into two mixed type dislocations for the glide in {111} planes. Eventually, only mixed type dislocations have been observed at the surface of thick GaSb layers.
Long Short-Term Memory Based Millimeter Wave Beam Change Prediction via Real-World Data

Beam management (BM) is a key component in fifth generation (5G), 5G-Advanced, and sixth generation (6G) millimeter-wave (mmWave) operations. Due to mobility or rotation of user equipment (UE), frequent beam measurements and search are needed at the UE based on also frequently transmitted reference signals (RSs). To enable mmWave BM with lower power consumed at the UE and less RS overhead, this paper proposes a machine learning (ML) assisted beam change prediction scheme based on long short-term memory (LSTM) facilitated models. Particularly, the ML-models are equipped at base station (BS) and use time series of UE reported beam qualities as inputs, which are based on the latest 3rd Generation Partnership Project (3GPP)5G new radio (NR) standards. The model outputs prediction on whether the strongest beam will be different from the currently reported one until the next applicable measurement occasion. This allows the system to operate with an extended beam measurement cycle greater than commercially popular ones with very limited beam mismatch errors. Our proposed ML-models are trained and tested via data collected using a cellphone equipped with Snapdragon 5G-mmWave Modem chipset and a prototype BS, both compliant with 3GPP Release-15 5G standards. Evaluation results show that the proposed scheme can accurately predict beam change instances and reduce over half of the power consumed at the UE for BM, which also demonstrate feasibility of ML based BM for 5GAdvanced and 6G communications.
Baseband Phase Noise Modeling and Analysis for 140GHz THz DFT-s-OFDM System

In this paper, the performance of the DFT-s-OFDM system is studied in the presence of phase noise for the THz wireless communications. We are conducting the studies to evaluate the phase noise effect in the THz system and to help it be used in system design, and this paper deals with baseband modeling procedures of the phase noise characteristics in the 140GHz frequency band. Firstly, we consider the phase noise effect of the DFT-s-OFDM system as a matrix form. Secondly, we elaborate the procedure for modeling phase noise by converting the analog phase noise model to its baseband equivalent digital model. For the accuracy of the phase noise model, we also consider the measured results from the data-sheet of the real oscillators. Finally, using the derived phase noise model, we perform link-level simulation on a 140GHz DFT-s-OFDM system extended from the specification of 5G NR, and evaluate its performance in presence of phase noise. We believe that our phase noise modeling procedure can offer a useful benchmark for optimal transmitter and receiver designs in DFT-s-OFDM systems with phase noise in THz frequency band.
Cross-subject Trials Reweighting for Enhancing Motor Imagery-based Brain-Computer Interface

Because of the non-steady state of EEG signals, there are differences in the distribution of electroencephalogram (EEG) data among different subjects. This distribution difference leads to a large error indirectly using the data of different subjects for training. This paper proposes a cross-subject trial reweighting (CSTR) method to reduce the distribution difference. CSTR assigns weights to each sample to narrow the maximum mean discrepancy between the source and target domains. CSTR is applied to the original EEG data samples, and can also be applied to samples after various feature processing. We use the motor imagery dataset to verify the effectiveness of the CSTR algorithm. The experimental results show that the source domain and the target domain become more similar after the trials are reweighted. The classification performance is improved using the reweighted data. The method proposed in this paper can improve the performance of the transfer learning brain-computer interface, reduce the calibration time of BCI, and promote the practical application of BCI.
An Improved Hunter-prey Optimization Algorithm and Its Application*

Hunter-prey optimization (HPO) algorithm is a new optimization algorithm proposed to simulate the behavior of leopards, lions and other predators in hunting deer and antelope. In order to solve the problems such as insufficient global optimization capability of HPO, easy to fall into local optimization, and low optimization accuracy, an improved Hunter-prey optimization (IHPO) algorithm is proposed. Firstly, Tent chaotic map is used to generate the initial population and increase the diversity of individuals, Secondly, in order to balance the ability of global search in the early stage and local search in the late stage, the enhanced sine cosine algorithm (ESCA) is integrated to adaptively select the population location update mode according to the conversion probability, Finally, Cauchy mutation strategy is adopted in the later stage of iteration to disturb the population position and enhance the ability of the algorithm to jump out of precocity. The simulation results of benchmark functions show that IHPO algorithm has better convergence accuracy and convergence speed. The effectiveness of the improved algorithm is further verified by the simulation experiment of pipe routing examples
Observational evidence of preferred flow regimes in the Northern Hemisphere winter stratosphere

Abstract Ten yeah of stratosphere geopotential height data are analyzed in an attempt to determine whether there are preferred flow regimes in the Northern Hemisphere winter stratosphere. The data are taken from Stratospheric Sounding Units on board NOAA satellites. The probability density estimate of the amplitude of the wavenumber 1 10-mb height is found to be bimodal. The density distribution is composed of a dominant large-amplitude mode and a less frequent low-amplitude mode. When the wavenumber 1 10-mb height data are projected onto the phase plant defined by the 10-mb zonal-mean winds and wavenumber 1 100-mb heights, three preferred regimes are evident. The small-amplitude mode separates into a strong zonal wind-weak wave regime and a weak zonal wind-weak wave regime. The large-amplitude mode is an intermediate zonal wind-strong wave regime. Transitions between the large-amplitude regime and the weak zonal wind-weak wave regime are found to be associated with major stratospheric warnings. The clust.
Vocal/Music Classification using Incremental Learning Approach

Multimedia Data Content Labeling has been a research area for a relatively long period. Researchers have spent a substantial amount of time classifying video sequences, music genres, and artists' classification domains. vocal/music tagging is prevalent in this field, but it is addressed chiefly in Western music. This paper mainly focuses on the musicvocal binary classification of every 15-seconds window of the Indian songs, specifically in the Hindi language. The increasing amount of data recorded by the Hindi song industry shows that old methods are not efficient enough for tagging. Incremental learning has been used quite widely now in multimedia data content labeling. This paper proposes an incremental learning-based approach developed explicitly for binary classification based on Hindi songs. The proposed algorithm is developed for music-vocal classification. The incremental learning approach showed significantly good results. Moreover, there are not enough datasets available for performing such tasks for Hindi songs. Thus, this paper also introduced a novel dataset containing window-based information labeled for each song for each artist. The proposed method achieved remarkable accuracy of 83.6% for music vocal classification. Cross-Language Testing is also performed to check if the model generalizes over other language songs.
Performance Analysis of Cat Swarm Optimization with Soft Discriminant Classifier for Diagnosis of Epilepsy using EEG Signals

A seizure caused by epilepsy is characterized by the rapid excitation of a significant number of neuronal cells in quick succession. Patients have a great deal of difficulties as a result of an unanticipated anomalous function that occurs in their brains. Due to the neurons' high rate of electrical discharge, the usual bodily functions are greatly perturbed. Electroencephalography (EEG), a visual representation of these electrical brain movements, is used to record them. In this study, dimensionality reduction and feature extraction algorithms are used to minimize the dimensionality of EEG data. The Power Spectral Density (PSD) and Singular Value Decomposition (SVD) algorithms are employed to lower dimensionality. The Cat Swarm Optimization (CSO) algorithm is employed as a feature extraction method. Softmax Discriminant classifier is used to identify epilepsy. Results show that the when PSD with CSO is identified with soft discriminant classifier model gives the best accuracy of 97.19%. When SVD with CSO is identified with soft discriminant classifier model gives the accuracy of 95.55%.
Time Series Data and Recent Imputation Techniques for Missing Data: A Review

The development of multisensory systems and the ongoing application of data collection technologies have both contributed to the explosion of time series data. However, due to many factors, undesirable missing data points are often encountered. The inability to analyse and model the missing data greatly hinders categorization and forecasting activities. Traditional techniques for processing time series data frequently add bias and make significant assumptions about the underlying data creation process, which can result in inaccurate development of prediction or classification models. The characteristic of the time series data needs to be well understood before applying the correct approach for imputation. This study aims to brief the types of time series data, and missing data mechanisms and also reviews several approaches to filling data gaps that are convenient for time series data. The review highlights current approaches in handling missing values at the data pre-processing stage for univariate and multivariate time series data together with the methods used to evaluate the performance of the imputation approach. It includes some advantages and drawbacks of these approaches practically. The results provide information which can be used to further develop a new imputation approach.
Maximum End-to-End Latency Minimization in UAV-Assisted IoT Networks

With the advancement in wireless communications and smart device technologies, the Internet of Things (IoT) will make future services and applications more automatic and intelligent. However, the transmission rate and range of IoT devices are restricted by the limited battery power, which makes it challenging to provide satisfying services for delay-sensitive applications. Thanks to the high flexibility of unmanned aerial vehicles (UAVs), we propose a UAV-assisted IoT network model, where the UAV acts as the relay to help IoT nodes transmit their packets to the access point to prevent the network from overloading. With the aim of minimizing the maximum endto-end latency, a joint UAV trajectory design and IoT device scheduling problem is formulated. As the problem cannot be formulated with explicit expressions, it is intractable to solve the problem with traditional model-driven optimization algorithms. Therefore, we adopt deep reinforcement learning to solve this problem, and propose a soft actor-critic (SAC) based algorithm for its high sampling efficiency. Numerical results demonstrate that the maximum network end-to-end latency is substantially reduced with the assistance of the UAV. Moreover, the proposed SAC algorithm is shown to possess higher sampling efficiency than the deep Q-network
Optimal Control by Pattern Search Optimization Method

In this work, we demonstrate the use of pattern search optimization method in numerically solving optimal control problems. Pattern search is a derivative free optimization method and can be used in cases where differentiability and/or continuity of the optimality system cannot be assumed. To demonstrate the results we formulate an optimal control problem which maximizes information diffusion in a heterogeneous graph while keeping the cost of external intervention to the minimum. Information diffusion is modeled as a susceptible-infected process. The formulated problem is discretized and solved numerically using the direct method employing a pattern search optimization technique. The solution methodology works effectively in both scale free and real world social graphs. Derivative free methods are useful when differentiability/continuity of optimality system cannot be assumed, or when numerical estimation of gradient and higher order derivatives is not accurate enough and leads to computational issues.
On the miniaturization of printed log periodic antenna using T-shaped dipoles

In this paper a compact printed log-periodic dipole antenna (PLPDA) with T-shaped dipoles is presented. Dipoles are bent to increase the electrical size of the dipoles while still maintaining the physical length. The antenna’s radiation resistance and radiation efficiency is improved. The antenna has an operational bandwidth from 0.85 to 6.5 GHz. When T-shaped dipoles are used, the dimension of the PLPDA is reduced by 56%. Impact of miniaturization on the antenna performance is studied through simulations. When T-shaped dipoles are used, the VSWR bandwidth increased by 22.1%, compared to conventional PLPDA.
Design of an Anti-Windup Equivalent-Input-Disturbance-Approach-Based Control System

This paper presents an anti-windup equivalent-input-disturbance (AEID) approach to eliminating both influences of disturbances and input saturation of a plant. The AEID approach has a conventional EID estimator and a newly added anti-windup structure. The EID estimator estimates the disturbances and uses estimated results to compensate for them. The anti-windup structure combats the influences of the input saturation. Numerical simulation verifies the effectiveness of the AEID approach.
STRESS-DEPENDENT HOLE EFFECTIVE MASSES AND PIEZORESISTIVE PROPERTIES OF P-TYPE MONOCRYSTALLINE AND POLYCRYSTALLINE SILICON

Piezoresistive effects of $p$-type polycrystalline silicon underline that longitudinal and transversal piezoresistive properties in monocrystalline silicon do not have the same physical origin, which is not accounted for in current models. This difference is highlighted by the study of the mechanical stress effect on the valence band, which shows that piezoresistive properties of $p$-type monocrystalline silicon can be explained in terms of both hole transfer between heavy- and light-hole valence bands and stress-dependent hole effective masses. The quantification of these phenomena points out that longitudinal piezoresistive properties are mainly due to the hole transfer, whereas transversal ones are mainly attributed to the effective mass change effects. This enables one to model $p$-type polycrystalline silicon piezoresistivity, in particular the sign change of the transversal gauge factor at high doping level.
An Efficient Algorithm for Plant Disease Detection Using Deep Convolutional Networks

Plant diseases and pests are important factors in determining crop yield and quality. Plant diseases are not only a threat to food security on a global scale but can also have devastating consequences for farmers whose livelihood depend on healthy crops. The detection of plant diseases is of fundamental importance in practical agricultural production. It controls the growth and health of the plant and ensures the regular operation and successful harvest of agricultural plantations. The disease affecting the plants is determined by factors such as the climate. This paper examines an alternative approach to developing a disease detection model supported by leaf classification using deep convolutional networks. Growth in computer vision present a scope to broaden and boost the practice of precision crop protection and expand the market for computer vision applications in precision agriculture, a completely unique form of training and therefore the technique used allows for quick and direct implementation of the system in practice. The database used in this paper consists of 77,000 images of healthy and infected plant leaves. We were able to train a CNN model for classifying plant diseases that is, they are present or not, and then another model was trained with YOLOv7 to detect the disease. The trained classification model achieved an accuracy of 99.5% and the detection model was able to achieve mA$P$, precision, recall of 0.65, 0.59and 0.65 respectively.