Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of User’s Learning Styles and Academic Emotions through Web Usage Mining

Digital learning environment has seen a considerate amount of growth as opposed to the traditional learning environment with a massive shift towards digitalization in day-to-day life. When teaching in person, one can get a deep understanding of how students are grasping information, and their performance can be monitored. On the other hand, this task becomes challenging when in digital learning process. Several techniques have been studied for identification of learning styles and academic emotions such as questionnaires, facial expression recognition, biometrics data, etc. This paper proposes a system which aims at identification and analysis of learning styles and emotional behaviour of users based on the techniques of web usage mining where web server logs are used for data capturing as they have many advantages over other techniques. The logs are further preprocessed and used for extraction of desired results using different machine learning methodologies like data clustering, classification to name a few. This will help to identify hidden patterns of users over the period of completion of course and ultimately improve the teaching learning efficiency.
The S- and C- band Low-power CMOS LNA Using the Current-reuse Technique

In this paper, a single-ended low-power Low Noise Amplifier for S- and C- band based on 90nm CMOS technology is proposed. The low noise amplifier was designed to better the performance of the low power consumption and low noise figure. To achieve low power consumption together with a low noise figure a current reuse technique is used. The LNA also provides good input and output of 50 Ohm matching. The designed LNA is supplied by 1 V and consumes only 6mA, this is appropriate for low-voltage applications in wireless receivers.
Binary Cross Z-Complementary Pairs With Flexible Lengths From Boolean Functions

In this letter, a novel construction of binary cross Z-complementary pairs (CZCPs) based on Boolean functions is proposed. CZCPs have been proposed to be used as training sequences in the spatial modulation system. Due to their zero correlation zone (ZCZ) properties for autocorrelation sums and cross-correlation sums, optimal channel estimation performance over frequency-selective channels can be achieved. In the literature, most constructions of CZCPs are based on existing Golay complementary pairs (GCPs); only one method is based on Boolean functions. However, the length of CZCPs from Boolean functions is very limited. In this letter, the proposed construction can generate CZCPs with more flexible lengths and large ZCZ widths.
Investigation of GaP Based Nanowires Optical Properties for Lasing Applications

This research is focused on the study and characterization of gallium phosphide based nanowires optical properties. Samples were grown by molecular beam epitaxy on Si (111) substrate and transferred to Menzel’s slide for optical measurements. Two different samples containing core-shell and axially heterostructured nanowires are studied. Analysis of the photoluminescence spectra demonstrate that depending on the NW geometry it can act as a Fabry-Perot cavity and provide good waveguiding properties. Emission spectra of the nanowires demonstrate high resistance to the excitation power. Investigation of the core-shell nanowire Raman spectra demonstrate shift of the TO mode with intense illumination due to possible strain effects induced by the temperature increase.
Orientation-Assisted Beam Management for Beyond 5G Systems

Finding the optimal transmit and receive beam pair for reliable communication can be challenging, especially in highly dynamic environments. Side-information from on-board sensors at the user equipment (UE) can be used to aid the beam management (BM) process. In this work, we use the orientation information coming from inertial measurement unit (IMU) for effective BM. Specifically, we use particle filter (PF) to fuse the reference signal received power (RSRP) information with orientation information. We perform extensive simulations using realistic ray-tracing channels, practical beam patterns, and various UE movement and rotation speeds. Simulation results show the proposed strategy can greatly improve the beam prediction accuracy and reduce the power loss caused by sub-optimal beam-selection.
Toward human-centered visual design based on sound symbolic information

Novel objects, such as a product with a visual appearance matched with an impression from an object's name, may facilitate the identification of the object and improve user experience. To optimize visual appearance based on an object name, the study proposes a machine learning approach for the automatic prediction of human impressions from word sounds. The proposed method utilized a support vector machine to predict shape labels (i.e., spiky/round/neutral) using acoustic and phonetic features extracted from word sounds. The method demonstrated that acoustic and phonetic features can capture human impressions of shape obtained from word sounds. Moreover, the study conducted a behavioral experiment and demonstrated that shapes matched with impressions from names facilitate recall of the association between names and visual appearance. In addition, the proposed method predicted the degree of facilitation of the memory process. Therefore, the acoustic and phonetic information-based approach exhibited potential for application in the optimization of visual design.
Performance Analysis of Multi-User Downlink PD-NOMA Under SUI Fading Channel Models

Power Domain Non Orthogonal Multiple Access (PD-NOMA) is a multiple access technique that offers spectral efficiency, low latency and user fairness in 5G networks. This paper evaluates the performance of multi-user downlink PD-NOMA in suburban environments under SUI fading channels. Baseband modulated user signals with phase shifts are allocated power levels in accordance with users distance before the superposition coding is performed at the base station. A multi-level successive interference cancellation (SIC) is performed at the receivers. Bit error rate (BER) performance against signal-to-noise ratio (SNR) for up to four users per cluster is compared for all the SUI models corresponding to different suburban terrains with varying vegetation densities. An upper limit on number of users per cluster that are supported at a benchmark BER and SNR values is also evaluated.
Detecting Organisms for Marine Video Surveillance

The way to better understand the marine life and ecosystems is to surveil and analyze the activities of marine life. Research on marine organisms is becoming increasingly popular because of the increased focus in recent years. In this paper, we design a novel framework, dubbed Efficient Marine Organism Detector (EMOD) for high-resolution marine video surveillance, to detect and monitor marine organisms in a realtime and fast fashion. Current state-of-the-art marine organism detectors are mainly based on computer vision techniques that make great progress in recent years, which essentially requires a relatively large amount of various data. The datasets used are from the National Oceanic and Atmospheric Administration (NOAA), including a total five annotated video datasets HabCam, MOUSS, AFSC DropCam, MBARI and NWFSC. Experiments are performed on these three datasets with current popular one-stage detection methods (RetinaNet and SSD) and two-stage detection methods (Faster R-CNN and Cascade R-CNN) in our marine detector respectively. Experimental results demonstrate that our framework is competitive and efficient.
Generalized Design Technique of Ultra-Wideband Transitions for Quasi-TEM Planar Transmission Lines Based on Analytical Models

A generalized design technique of ultra-wideband planar transitions using EM-based analytical models is presented in this paper. Among various planar transmission lines, each transmission line has unique advantages over other line types, and a microwave component in a system can be designed to perform much better if it is implemented on a certain type of transmission line. Therefore, low-loss and high-performance transitions between transmission lines, with easy integrability with the main circuit board, are needed. The proposed transition design technique uses the Schwarz-Christoffel transformation, which is one of conformal mapping methods, and can be applied to design any planar transition between a pair combination of planar transmission lines based on TEM or quasi-TEM waves. To optimally match the characteristic line impedance and smoothly transform the electromagnetic field distribution between the planar transmission lines, entire cross-sections through the planar transition should be analyzed with proper models. In this paper, the cross-sections of planar transitions are categorized into 4 cross-sectional models, where each cross-section is divided into multiple regions for the analysis to obtain line capacitance. Therefore, for the 4 cross-sectional models, 7 types of basis structures are identified to obtain their capacitances by applying conformal mapping. By adding capacitances of a combination of the 7 analysis types, the total line capacitance, thus the characteristic line impedance, of any cross-sectional model of the planar transition can be obtained. The characteristic line impedance of each cross-sectional model calculated with the proposed analytical formulas is compared with the 3D EM calculations, and it is found that the deviated values are mostly well under 6%. This proposed technique enables to design various planar transitions efficiently and quickly for the maximum performance without parameter tuning trials, by providing optimal impedance matching and smooth field transformation up to mm-wave frequencies.
Chirp-Convolved Data Transmission

We propose a multicarrier chirp waveform, which can be generated by a circular convolution between data modulation symbols and a transmit filter, and show that it can equivalently be represented as discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) with frequency domain chirp filter. The chirp is defined by a complex exponential function where the phase follows a second-degree polynomial and its coefficients can be chosen to maximize the performance on a fading channel and to generate sets of orthogonal signals. The results show that the waveform outperforms OFDM and DFT-s-OFDM in terms of bit error rate (BER) and block error rate (BLER) on a time-frequency selective channel.
A Gaussian Kernel-Based Spatiotemporal Fusion Model for Agricultural Remote Sensing Monitoring

Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. Due to the tradeoff between a single sensor's spatial and temporal resolutions and the impacts of cloud coverage, the time series NDVI data cannot serve well for precision agriculture. In this study, a Gaussian kernel-based spatiotemporal fusion model (GKSFM) was developed to fuse high-resolution NDVI (Landsat) and low-resolution NDVI (MODIS) to produce a daily NDVI product at a 30-m spatial resolution. Considering that the NDVI curve of crop in each growing season can be characterized by Gaussian function, GKSFM used the Gaussian kernel to fit the nonlinear relationship between the high-resolution NDVI and the low-resolution NDVI, to obtain a more reasonable temporal increment. The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/noncropland and different crop phenology. In addition, GKSFM was also applied for crop mapping of Mishan County by fusing the NDVI images during the crop growing season. This study demonstrates that the accuracy of the proposed method can be improved in the midseason of crops.
Communication Infrastructure and Cloud Computing in Robotic Vessel as-a-Service Application

The current trend in robotics is to enhance robustness against uncertainties through complex modern control methods, applied to either single- or multi-agent systems. While indeed these approaches provide noticeable performance improvements, their implementation requires, among others, enclosed environments, where the operator has full access to the control systems of the robot during execution. With constant improvement of the Internet of Things, real-time systems needed different software architectures and communication systems to enable their remote operation, with the same degree of flexibility when operating the systems in-situ. The paradigm shifted towards cloud computing, which moved the computational effort needed for various real-time processing algorithms on the server-sided application and focused on having lightweight interfaces between components, with the exception of the lower-level control loops, which remained within the processing units of the robotic systems. This paper describes the software architecture and communication system for a state-of-the-art service to enable, support and monitor different robotics applications. The case study presents all features of a small-sized but scalable robotics application, having multiple challenges when building a service oriented platform: autonomous robots, over- and underwater long-distance communication, sensor fusion and command and control of multiple users. The goal is to create a centralised cloud computing environment, with decentralised microservices and redundant resources, supporting a plethora of port-specific operations conducted with the help of waterborne robotics with different equipment configurations, that can be remotely operated and monitored in real time over cross-platform, lightweight applications.
Application of Vehicle Detection Based On Deep Learning in Headlight Control

When driving at night, vehicle lights are the greatest guarantee for driving safety. Drivers often turn on the high beams to make the oncoming vehicle unclear, turn on the high beams of the oncoming vehicle to reduce their visual range or turn on the high beam when driving behind. The lights prevent themselves from being able to check the rear view through the rear mirror and cause traffic accidents. In addition, drivers often fail to keep a safe distance from the traffic, which causes serial accidents. In response to this problem, this paper proposes a deep learning-based image recognition in the headlight control system. Vehicles using this system can detect the vehicle ahead in real time when the driver is driving the vehicle, and calculate the safety distance of the vehicle ahead, and judge at night Whether there are vehicles in the front and oncoming lanes to determine whether to turn on the high beam, so as to reduce light damage and safe distance traffic accidents.
Minke Whale Detection in Underwater Imagery using Classification CNNs

A predictable aggregation of dwarf minke whales occurs annually in the Australian offshore waters of the northern Great Barrier Reef in June–July, which has been the subject of a long-term photo-identification study. Researchers from the Minke Whale Project (MWP) at James Cook University collect large volumes of underwater digital imagery each season (e.g. 1.8TB in 2018), much of which is contributed by citizen scientists. Manual processing and analysis of this quantity of data had become infeasible, and Convolutional Neural Networks (CNNs) offered a potential solution. Our study sought to design and train a CNN that could detect whales from video footage in complex near-surface underwater surroundings containing multiple people, boats, research and recreational gear. We modified known classification CNNs to localize whales in video frames and digital still images. The required high detection accuracy was achieved by discovering an effective negative-labeling training technique. This resulted in a less than 1% false-positive detection rate and below 0.1% false-negative rate. The final operation-version CNN-pipeline processed all videos (with the interval of 10 frames) in approximately four days (running on two GPUs) delivering 1.95 million sorted images.
Transformation of iron oxide and hydroxide phases and its effects on contaminant mobility [an abstract of dissertation and a summary of dissertation review]

Iron oxides and hydroxides [iron (hydro)oxides] are one of the most common phases in the earth's crust. Due to their ubiquity in the natural environment, they have long been recognized for their ability to regulate the concentrations of hazardous elements in nature. Furthermore, the relative ease with which they can be synthesized under controlled conditions has made them attractive materials for industrial applications like water-treatment and contaminant remediation. Iron (hydro)oxides, in particular, are expected to play an important role in controlling the release of radionuclides from geological disposal environments. One of radionuclides present in spent fuel wastes is Se-79, which exists as highly mobile Se(IV) oxyanions under alkaline and partly reducing conditions expected in geological repositories. The iron (hydro)oxides present in steel corrosion products as well as in the surrounding host rock could provide a pathway to immobilize it. However, the long-term behavior of radionuclides like Se-79 is strongly dependent on the transformation of iron (hydro)oxide substrates, which, in turn is also affected by the presence of other foreign ligands in the system. One of the most important ligands known to affect iron (hydro)oxide transformation is Si, which is derived from the dissolution of the vitrified waste form as well as from the surrounding silicate host rock. Taking into account the influence of iron (hydro)oxide transformation as well as that of foreign ligands is important to formulate more reliable safety cases to predict the long-term stability of waste disposal environments.
Performance Comparison of Different EEG Analysis Techniques Based on Deep Learning Approaches

Deep learning models using neural networks are capable of finding hidden patterns and their associations without assistance of domain experts. Brain-computer interface (BCI) is one of the popular research areas and has seen a variety of research approaches in recent years. Combining BCI research with deep learning has opened doors to many interdisciplinary applications in the aviation industry, passenger safety and many more. Electroencephalogram (EEG) analysis is a crucial part in the research of BCI. This paper aims to present a survey of published works on EEG analysis techniques in the past few years. The paper gives the different deep learning techniques used in the analysis of EEG signals. In this paper, we propose a brief study and comparison of existing methodologies for EEG signal analysis using deep learning models.
Multimodal Embeddings From Language Models for Emotion Recognition in the Wild

Word embeddings such as ELMo and BERT have been shown to model word usage in language with greater efficacy through contextualized learning on large-scale language corpora, resulting in significant performance improvement across many natural language processing tasks. In this work we integrate acoustic information into contextualized lexical embeddings through the addition of a parallel stream to the bidirectional language model. This multimodal language model is trained on spoken language data that includes both text and audio modalities. We show that embeddings extracted from this model integrate paralinguistic cues into word meanings and can provide vital affective information by applying these multimodal embeddings to the task of speaker emotion recognition.
Machine Learning based Malaria Prediction using Clinical Findings

Even today, Malaria is the most deadly disease in Asia and sub-Saharan Africa and particularly in Senegal. This is mainly due to inadequate medical care support with frequent late and error-diagnoses by medical professionals. Besides, mostly used diagnostic standards such as the rapid diagnostic test is not fully reliable. With the development and widespread acceptance of automated systems in the healthcare system, machine learning algorithms can support medical professionals in their decision-making procedure. An experimental analysis of different machine learning techniques to predict Malaria is proposed in this work. These techniques attempt to determine whether or not a patient suffersfrom Malaria using various clinical findings like signs and symptoms. The algorithms’ efficiency has been thoroughly validated and analysed over two actual data sets of malaria patients’ taken from Senegal. The results obtained show that Random Forest, Support Vector Machine with Gaussian Kernel and Artificial Neural Networks are promising and offer the best overall accuracy to predict the appearance or not of the disease with precision, recall and F1-score at least equal to 92%, 85% and 89% respectively on both datasets on which they outperform the Rapid Diagnostic Test.
Evaluation of cellular and circulatory antioxidant- and glutathioneassociated enzymes in patients with acute coronary syndrome

Destabilization of atherosclerotic plaque in the coronary artery is the result of Oxidative Stress (OS) that damage the myocardial tissues in Acute Coronary Syndrome (ACS). This study investigated the activities of certain circulatory and cellular antioxidantand glutathione-associated enzymes in ACS patients in comparison to a control group. Standard assay methods were followed to evaluate the activities of Superoxide Dismutase (SOD), catalase, Glutathione-S-transferase (GST), Glutathione Peroxidase (GPx) and Glutathione Reductase (GR). For data analysis, the categorical variables were measured in percentages, and continuous variables were expressed in means and standard deviations. The ACS patients had significantly higher activities of circulatory SOD, GPx and GST compared to the controls (4.36 ± 2.28 U/mL, 49.20 ± 14.12 U/mL and 5.02 ± 3.03 U/mL versus 2.87 ± 1.28 U/mL, 21.53 ± 10.80 U/mL and 3.03 ± 1.99 U/mL, respectively) but their catalase and GR activities were significantly lower. While the catalase activities in the erythrocyte and leukocyte lysates were similar in both groups, the leukocyte SOD activity was significantly lower in patients. A significant positive correlation was found between the GR and catalase activities in patients. The circulatory enzymes SOD, GPx and GST are over-expressed in controlling excessive OS, while the reduced activities of catalase and GR could be the consequence, suggesting therapeutic potentials of using enzymes in reducing OS-mediated endothelial injury in ACS patients.
A New Wideband, Low Insertion Loss SiGe Digital Step Attenuator A New Wideband, Low Insertion Loss SiGe Digital Step Attenuator

This paper presents a new, wideband digital step attenuator (DSA) implemented using SiGe HBTs. The vertical profile of the SiGe HBT presents small parasitic shunt capacitance and, thus, the loss from the switches in a switched-type attenuator is significantly reduced. For a 6-bit DSA covering a maximum attenuation of 31.5 dB with 0.5 dB steps, reduced T-type cells with reverse-saturated SiGe HBTs are used for low attenuation cells and switched pi-type cells with novel anti-parallel (AP) SiGe HBT pair series switches are used for high attenuation cells. In addition to the symmetry of the AP SiGe HBT pair, reduced T-type and switched pi-type cells are placed alternately to distribute parasitic components and to achieve better overall symmetry. As a result, state-of-the-art performance is achieved over a wide bandwidth, from DC-to-60 GHz, with an insertion loss of 7.5 dB at 50 GHz, 31.5 dB of attenuation range and less than 2.8 mW of power consumption.