Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG-Based Driving Fatigue Recognition Using Hybrid Deep Transfer Learning Approach

One of the main causes of traffic accidents is driving while tired. World Traffic Safety Organization statistics indicate that driving while tired leads to 35-45% of road accidents and directly causes 1550 deaths, 71000 injuries, and 12.5 billion dollars in economic damage every year. To detect fatigue over time during driving, it is crucial to design an accurate and efficient analysis architecture. Physiological signals can significantly reduce the subjectivity and individuality of fatigue in drivers compared to methods like face analysis or questionnaire design. EEG signals contain valuable information about fatigue. Fatigue detection using brain signals is still a challenge due to the significant differences in EEG signals among people and the difficulty in collecting enough signal samples during fatigue. To reduce the level of classification error, hybrid deep learning was used in this paper as an efficient and fast method. By combining dilated-ResNet and Inception module, the approach is faster than other learning methods and provides satisfactory accuracy for fatigue detection. Windowing is used to receive signals from a limited number of channels. Windows are selected based on different lengths and then their spectrogram is created using short-time Fourier transform (STFT). More than 99% accuracy was achieved by the suggested approach. In order to recognize people's fatigue, the method has used a small number of channels that can be generalized and interpreted.
Dielectric Spectroscopy: Revealing the True Colors of Biological Matter

Accurate characterization of biological matter, for example, in tissue, cells, and biological fluids, is of high importance. For example, early and correct detection of abnormalities, such as cancer, is essential as it enables early and effective type-specific treatment, which is crucial for mortality reduction <xref ref-type="bibr" rid="ref1">[1]</xref>. Moreover, it is imperative to investigate the effectiveness and toxicity of pharmaceutical treatments before administration in clinical practice <xref ref-type="bibr" rid="ref2">[2]</xref>. However, biological matter characterization still faces many challenges. State-of-the-art imaging and characterization methods have drawbacks, such as the requirement to attach difficult-to-find and costly labels to the biological target (e.g., COVID-19 rapid tests), expensive equipment (e.g., magnetic resonance imaging), low accuracy (e.g., ultrasound), use of ionizing radiation (e.g., X-rays), and invasiveness <xref ref-type="bibr" rid="ref3">[3]</xref>. The characterization of biological matter using microwave (<italic>µ</italic>W), millimeter-wave (mmW), and terahertz (THz) spectroscopy is a promising alternative: it is label-free, does not require ionizing radiation, and can be noninvasive. Moreover, there is a significant difference in how different biological materials absorb, reflect, and transmit electromagnetic (EM) waves <xref ref-type="bibr" rid="ref4">[4]</xref> that is due to the difference in their dielectric properties. The dielectric properties are described by the frequency-dependent material parameter called the complex permittivity <inline-formula><tex-math notation="LaTeX">$\mathbf{\varepsilon}\left({\mathbf{f}}\right){,}$</tex-math></inline-formula> which expresses how the material responds to an external oscillating electric field. The complex permittivity of a material determines how the material absorbs, reflects, and transmits EM waves at different frequencies (<xref ref-type="fig" rid="fig1">Figure 1</xref>). Since each biological material’s permittivity spectrum is different, it acts as an EM fingerprint. A material’s complex permittivity can be calculated from the reflection and transmission of EM waves through the material, described by the S-parameters, which can be measured using a vector network analyzer (VNA) transmitting and receiving EM waves over a range of frequencies. The amplitude and phase of the transmitted and reflected EM waves at different frequencies are influenced by different underlying biological effects at different scales. That causes the entire spectrum to provide information from the supracellular to the molecular and even atomic scale.
Energy Efficiency Analysis of FSO Backhauled Uplink NOMA System

Free space optical (FSO) communication system is a groundbreaking technology in the field of communication systems. Non-orthogonal multiple access (NOMA), on the other hand, currently being used in fifth-generation (5G) wireless systems, is a powerful technique for establishing communication for multiple users using the same time and frequency resources. In this paper, the performances of NOMA and orthogonal multiple access (OMA) in an FSO-based communication system are studied. In the FSO communication system, two base station units are considered to be connected to a central unit for FSO backhauling using an uplink-fixed NOMA scheme. Bit error rate (BER), ergodic capacity, and energy efficiency (EE) performances of the NOMA-based FSO system are studied and compared with an OMA-based FSO system. It is found that the NOMA-based system provides approximately 10% of the ergodic capacity gain and increases EE by 37%-60% for a given BER compared to the OMA-based system.
Which Policy is Used by the CNS to Control Human Standing Posture: Intermittent or Impulsive Control?

The aim of this research is to model the function of the Central Nervous System (CNS) when controlling human standing posture which paves the way for designing external skeletal structures for rehabilitation or in an industrial direction, for building human-like biped robots. We used a simple inverted pendulum with one degree of freedom to model body skeletal dynamics. Three different feedback control policies were used to describe the function of the CNS, and the stability of the whole closed-loop model under each policy was studied. The policies we considered for the CNS as a controller are: 1) intermittent control, 2) impulsive control, and 3) hybrid control, which is a combination of intermittent and impulsive. The stability of the closed-loop model under each policy was evaluated numerically using Lyapunov stability criteria. Our simulation results indicate that while the intermittent policy results in a stable system, impulsive control is more efficient in terms of energy consumption. Consequently, a hybrid control strategy will result in a system that compromises between stability and energy efficiency.
Ohm's Law for a current sheet

We derive an Ohm's law for single-particle motion in a current sheet, where the magnetic field reverses in direction across the sheet. The result is considerably different from the resistive Ohm's law often used in MHD studies of the geomagnetic tail. We extend our single-particle analysis to obtain a self-consistency relation for a current sheet which agrees with previous results. Our results are applicable to the concept of reconnection in that we obtain the electric field parallel to the current for a one-dimensional current sheet with constant normal magnetic field. Dissipated energy goes directly into accelerating particles within the current sheet.
Scattering Analysis of Finite Periodic Array in the Vicinity of Arbitrary Object Using Hybrid Macro Basis Function Method

A hybrid macro basis function (MBF) method is proposed to analyze the finite periodic array in the vicinity of an arbitrary object. The array cells and object are modeled using the low order basis functions firstly. Afterwards, the MBFs defined on the cells are constructed using the modified accurate subentire domain basis function method. The MBFs on the object can be obtained using the characteristic basis function method. The total number of MBFs defined on the array cells and object is far less than that of the low order bases. Another advantage of the proposed method is that multiple excitations can be efficiently computed. Simulation results show that good agreement between the proposed method and other techniques is reached. In addition, computational efficiency is validated.
Segmentation of Corpus Callosum using Attention U-Net Architecture for MRI Scan

In this paper, an attention U-net-based deep learning method for the semantic segmentation of the corpus callosum (CC) from brain Magnetic Resonance Imaging (MRI) scans is proposed and implemented. Most neurological analyses benefit greatly from the structural data that can be obtained from the segmentation of brain MRI images. The proposed technique has a deep supervised encoder-decoder architecture and a redesigned attention network. Slice by slice, the model analyzes an entire MRI image to determine the ideal mask for corpus callosum. The model was trained using the ABIDE and OASIS datasets, and its performance was analyzed for different test samples using a standard measure of dice coefficient, yielding a dice accuracy of 93.5%. Visual samples of predicted CC from brain MRI are given and contrasted with the original ground truth to help understand how well the model performs. The findings demonstrate that the suggested approach is one of the best segmentation techniques, as it achieved very competitive CC segmentation performance even with a single model.
Human Speech Emotion Recognition Using CNN

Human speech emotion recognition is known to be the procedure of identifying emotion from the natural speech. It has become necessary to understand and detect the emotions of humans through various ways to provide a better user experience for the consumers. However, individuals have a wide range of diversity in their capacity of expressing emotions. Also, it may differ depending on how those emotions are being identified. Previously, different methodologies and techniques have been used to identify human emotions. An innovative convolutional neural network (CNN) based system for recognizing human speech and emotions is presented in this paper. Using a potent GPU, a model is constructed and fed with an unprocessed speech from a specified dataset for training, classification, and testing. The overall result was 94.38% which is quite better and surpasses many other models.
Sentiment Analysis on COVID-19 Tweets

The global proliferation of COVID-19, as well as the growth of platforms as a primary source of information for many individuals, has elicited a wide spectrum of reactions. However, keeping up with this mass scenario is difficult. A large number of people offer their opinions and perspectives on current events on social media, making it difficult for a human to read and comprehend everything. Tweets disseminate a great deal of information. Using public Twitter comments, our study attempted to conduct a sentiment analysis of the entire discourse about COVID-19 in a paper. We improved on earlier methodologies and methods for sentiment analysis. Our primary goal is to examine tweets concerning COVID-19 from the previous year using natural language processing and neural network methods. We used a multiclass dataset and applied it to BOW, TFIDF, and One Hot Encoding. Furthermore, after training four distinct classifiers with these different pre-processing algorithms in each classifier, we performed a competitive analysis and found RoBERTa as the best performing with 90% accuracy.
An Enhanced Deep Neural Network Enabled with Cuckoo Search Algorithm for Intrusion Detection in Wide Area Networks

A diversity of harmful software has been created as a result of the dramatic increase in internet usage, posing major risks to computer security. There is a great probability that the numerous computing operations performed through the network will be interfered with or altered, and as a result, effective intrusion detection systems are imperative. In addition, the attacks on the network are unpredictable, something that emphasizes the value of creating effective classification and prediction models. Machine learning (ML) and Deep Learning techniques have been used to evaluate datasets for intrusion detection systems (IDS). The employment of the DL-based approach enabled by feature selection helps to address challenges with data quality, handling high-dimensional data, and other related issues. Therefore, due to the large nature and volume of the IDS datasets, and the ability of DL-based models to learn categories incrementally through their hidden layer architecture to produce more accurate results in big data, this study proposes a Long-Short-Term-Memory (LSTM) model, and to further enhance the classification capacity of the projected DL method, the cuckoo search algorithm was introduced to select optimal features from the wireframe. The accuracy and subsequent detection of the suggested model positive and negative rates were evaluated. The experimental results show that the LSTM outperformed some other existing models with the highest classification accuracy of 99.7% and an error rate of 0.006.
A Novel Machine Learning Algorithm: Music Arrangement and Timbre Transfer System

Early neural network models have been used in image recognition and analysis. As time moved on, neural network usage expanded into music generation. Most generative models of music only focused on single-track generation. While much development had been produced, multi-track music composition and multi-instrumental band arrangements had been ignored. This paper introduced the Music Arrangement and Timbre Transfer System (MATTS) model, which further optimized existing models of Music Transformer and Differentiable Digital Signal Processing (DDSP) library to achieve computer music composition and instrumental band arrangements while maintaining realistic elements such as melodic sequences, repetitions, and imitations. Not only did it maintain realistic aspects, but our model also offered controls of pitch and amplitude. There were two forms of composition, where one with preconditioned user input and one without. MATTS could unconditionally compose and arrange a band to play the music it generated.
Wide field camera 3 ground testing and calibration

Wide Field Camera 3 (WFC3), a panchromatic imager developed for the Hubble Space Telescope (HST), is fully integrated with its flight detectors and has undergone several rounds of ground testing and calibration at Goddard Space Flight Center (GSFC). The testing processes are highly automated, with WFC3 and the optical stimulus, which is used to provide external targets and illumination, being commanded by coordinated computer scripts. All test data are captured and stored in the long-term Hubble Data Archive. A full suite of instrument characterization and calibration tests has been performed, including the measurement of key detector properties such as dark current, read noise, flat field response, gain, linearity, and persistence, as well as instrument-level properties like total system throughput, imaging quality and encircled energy, grism dispersions, IR thermal background, and image stability. Nearly all instrument characteristics have been shown to meet or exceed expectations and requirements.
CNN-Based Human Identification Method Using Image Photoplethysmographic

Biometrics has received extensive attention due to its accuracy and convenience. However, commonly used biometric features still have deficiencies. Facial recognition is a potential threat to privacy, and iris recognition or heartbeat recognition requires specific acquisition equipment, resulting in additional costs. To address this issue, we proposed a novel biometric identification method using image photoplethysmographic (IPPG). IPPG signal is easy collection with a consumer camera and the pixel-averaging operation to extract IPPG signal will remove private facial information. As a vital sign, IPPG signal is difficult to fake using abiotic prostheses. We constructed a CNN-based identification with IPPG (ID-IPPG) to verify the performance of IPPG signals in human identification. The proposed model achieves 97.3% accuracy on IPPG signals dataset containing 12 subjects. Moreover, the model can effectively perform in living body detection. The results demonstrate that IPPG signals contain individual physiological information and the ID-IPPG has high accuracy and security for human identification.
Fusion of Shallow and Deep Features for Classifying Skin Lesions

A skin lesion is an unusual change of skin tissues. While this can be caused by harmless skin diseases, there is also the chance of the lesion being cancerous. Skin cancer is one of the most common and deadly cancers in the world, which is caused by exposure to the ultraviolet radiation emitted by the sun. Due to the difficulty in visually differentiating between harmless and cancerous skin lesions, people are less likely to get medical attention straight away. Early diagnosis is crucial to ensure an effective treatment. Clinical and dermoscopy based diagnosis of cancerous skin lesions is costly, painful and sometimes inaccurate. Various researches report performing the classification of skin lesions using image processing techniques. Previous works in this domain are plenty, which reported fairly good results, where image processing and the use of both machine learning and deep learning models are seen. In this research, we propose a novel method which focused on important feature extraction, and fusing multiple features to improve the classification of malignant skin cells using traditional machine learning models, despite having imbalanced data distribution. The ISIC 2018 challenge dataset HAM10000 was used in our work. After preprocessing, we extracted shallow and deep features from the images. Shallow features consisted of position-wise color features and Scale Invariant Feature Transform (SIFT) features. Deep features were extracted by a transfer learning model MobileNetV3, which is pre-trained on Imagenet. These features were combined to form a more representative feature for the data. We parameter tuned five machine learning classifiers to do a binary classification on the processed data. The best accuracy, 81%, was obtained by using Support Vector Machine with an f1-score of 68%. Second best results were achieved by Random Forest Classifier, with an accuracy and F1-score of 80% and 67% respectively.
Application of Window Sliding ARIMA in Wind Speed and Solar Irradiance Forecasting

For better management and integration of renewable energy to the existing grid system, its accurate prediction is an inevitable requirement. For such a purpose, in literature, the statistical ARIMA model is often suggested to analyze wind speed and solar irradiance values. The present study explores window sliding ARIMA (WSARIMA) for energy prediction and reports its performance with respect to the conventional ARIMA method. The wind speed and solar irradiance data (2000–2014) from two test sites, namely Dhanora (Madhya Pradesh) and Nowlaipalle (Telangana) are used for the demonstration. It is observed that both datasets for both variables (wind speed and global horizontal irradiance, GHI) exhibit weak stationarity. The parameters for the ARIMA method are obtained through grid-search technique. Then, the proposed WSARIMA approach is applied to both datasets and results are noted. Based on the RMSE values, the WSARIMA method is found to be superior for both wind speed and GHI prediction. The involvement of sliding windows essentially incorporates seasonal fluctuations more productively in both data variables–wind speed and GHI. Therefore, the present study strongly recommends the WSARIMA model for energy prediction.
Point Cloud Alignment Method Based on Improved ISS-ICP Algorithm

For the traditional Iterative Closest Point (ICP) algorithm, its registration efficiency is low and the initial position of the registered point cloud is high. Accordingly, a point cloud registration method combining the optimized Intrinsic Shape Signatures (ISS) algorithm with the improved ICP is proposed. Specifically, the voxel filter is used to sample the original point cloud, then the key points are extracted by optimizing the search radius of the ISS algorithm, and described by fast point feature histogram (FPFH), and the corresponding relationship is established according to the feature. Subsequently, the normal features and the RANSAC algorithm are fused to eliminate the mismatching point pairs, and the initial transformation matrix is obtained by singular value decomposition(SVD). Finally, the ICP algorithm with median distance constraint is used to complete the precise registration. Experiments suggest that the accuracy and efficiency of the proposed algorithm are significantly improved compared with the traditional ICP algorithm.
Physiological Indicators of The Relation Between Autistic Traits and Empathy: Evidence From Electrocardiogram and Skin Conductance Signals

Difficulty in empathy is thought to be one of the problems in people with autism spectrum disorder (ASD), leading to impairment in social abilities and communication. However, despite the recent evidence on the effect of physiological bodily states on affective experiences, the exact role of physiological signals on different aspects of empathy (i.e., cognitive and affective empathy), as well as empathy dysfunction in ASD is yet unknown. To tackle this problem, in this study, 36 neurotypical subjects with different levels of autistic traits, participated in a well-established empathy for pain task, while Electrocardiogram (ECG) and Skin Conductance (SC) signals were recorded. Several features were extracted from each signal. Our results indicated that both cognitive and affective empathy are positively related to a higher level of cardiac activity (e.g., negative correlation with R-R interval) and arousal (e.g., positive correlation with average SC). More importantly, higher level of autistic traits, measured with Autism Quotient (AQ), was negatively correlated with Heart Rate Variability as measured with HRV-RMSSD and variability in tonic SC. Finally, we classified the participants into groups with high and low cognitive empathy, affective empathy, and level of autistic traits and investigated the extent to which machine learning approaches can automatically classify participants based on ECG and SC extracted features. Using a Support Vector Machine, reasonable results were obtained (in the range of. 73 to. 84), proving the possibility of implementing automatic detection systems for classifying subjects with different levels of autistic traits. Our results are suggestive of the effect of bodily simulation on empathy, and how the inability to regulate physiological signals leads to empathy dysfunction in individuals with high autistic traits.
Defect Detection of Printed Circuit Board Based on Light-weight YOLOv5s

For small target detection of circuit board defects, traditional detection methods have problems such as false detection, missed detection and slow detection speed. Although excellent models for detecting small targets exist in mainstream algorithms based on deep learning, their network structures are complex, computationally intensive and detection efficiency is low. In order to solve the above problems, this paper proposes a light-weight printed circuit board defect detection method based on YOLOv5s. The method uses an improved spatial pyramid pooling instead of the CSP module in the Backbone stage to provide multilevel perceptual fields while significantly reducing the computational effort. Secondly, a residual structure is introduced in Backbone and Neck to enable the network to obtain a better feature learning capability and improve the stability of training while accelerating the network convergence. Finally, the model parameters, calculation amount, mAP and AP with different defects of the improved algorithm in this paper are compared with other mainstream algorithms. The experimental results show that the improved algorithm in this paper greatly reduces the model parameters and calculation amount and improves the detection efficiency under the condition of high accuracy. Compared with other algorithms, it has obvious advantages and provides a new method for PCB defect detection.