Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon assisted water electrolysis for hydrogen generation

Carbon Assisted Water Electrolysis (CAWE) is an energy efficient process in that H2 can be produced at lower applied voltage (∼1.0 V) compared to nearly 2.0 V needed for ordinary water electrolysis for the same H2 evolution rate. In this process, carbon is oxidized to oxides of carbon at the anode of an electrochemical cell and hydrogen is produced at the cathode. These gases are produced in relatively pure state and would be collected in a separate chamber. In this paper, we present the results of influence of various operating parameters on efficiency of CAWE process. The results showed that H2 can be produced at applied voltages Eo as low as 1.0V (vs. SHE) and its production rate is strongly dependent on the type of the carbon used and its concentration in the electrolyte. It has also been found that the performance of CAWE process is higher in acidic electrolyte than in alkaline electrolyte.
The Effectiveness of Collaboration Using the Hackathon to Promote Computer Programming Skills

A model learning environment for a 21st-century Information Technology (IT) graduate comprises of both technical and soft skills. New trends are emerging on how to develop this technical and soft skills. Collaborative learning and social cognitive theory were adopted as a theoretical foundation for this study as limited studies have been done to find out which frameworks has been consistent to determine students' computer programming experience using the Hackathon. This paper aim to evaluate the effectiveness of collaboration in a Hackathon environment to learn Computer Programming (CP) among IT students. The objectives of the study are: (i) to examine the influence of collaboration on students' CP skills (ii) to examine the influence of Hackathon on students' computer programming skills (iii) to study the statistical association between collaborative learning and Hackathon on students' CP skills. 80 students participated in this study through a survey method and the responses was analyzed using SPSS software. The results of the study indicate that students were satisfied with their collaborative learning experience and believed that the Hackathon approach will help them in developing computer programming technical and soft skill. This study develops a model and contributes to empirical studies on Hackathon as most studies in this field are based on anecdotal findings.
A Comparative Study of Machine Learning and Deep Learning in Network Anomaly-Based Intrusion Detection Systems

This paper presents a comparative study of Machine learning and Deep learning models used in anomaly-based network intrusion detection systems. The paper has presented an overview of the previous work done in the field of ML and DL IDS, then an overview of the used datasets in reviewed literature was presented. Moreover, ML and DL models were tested on the KDD-99 dataset, and performance results were presented, compared, and discussed. Finally, areas of future research of critical importance are proposed by the authors.
Emotional Speaker Recognition based on Machine and Deep Learning

Speaker recognition is a method which recognise a speaker from characteristics of a voice. Speaker recognition technologies have been widely used in many domains. Most speaker recognition systems have been trained on normal clean recordings, however the performance of these speaker recognition systems tends to degrade when recognising speech which has emotions. This paper presents an emotional speaker recognition system trained using machine and deep learning algorithms using time, frequency and spectral features on emotional speech database acquired from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). We trained and compared the performance of five machine learning models (Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and k-Nearest Neighbor), and three deep learning models (Long Short-Term Memory network, Multilayer Perceptron, and Convolutional Neural Network). After the evaluation of the models, the deep neural networks showed good performance compared to machine learning models by attaining the highest accuracy of 92% outperforming the state-of-the-art models in emotional speaker detection from speech signals.
Adrisya Sahayak: A Bangla Virtual Assistant for Visually Impaired

Visually impaired persons have to encounter numerous complications in their mundane activities for imperfect vision. Though there are different technologies, Systems interaction procedure or expenditure is the key factor here. To provide a solution to this problem, we have introduced a methodology of a Bangla virtual assistant ‘Adrisya Sahayak (Invisible Helper)’. As a desktop application, it is especially for Bangla speaking visually impaired persons to reduce the difficulties in utilizing a computer, peripheral devices of the computer and home appliances. Bangla is one of the most spoken languages globally and the native language of Bangladesh and a few states of India. A big portion of them is illiterate and not capable of communicating in English. That’s why existing virtual assistants in the English language will be tough to use by them. The current user-independent system presented here will provide the opportunity to perform several tasks of a computer in the indoor environment via Bangla human voice commands.
Input-Adaptive and Regulated Multi-Output Power Management Unit for Wireless Power Reception and Distribution in Multi-Unit Implantable Devices

An input-adaptive and regulated power management unit with multiple outputs is presented for multi-unit implantable medical devices (IMDs), which are powered wirelessly. In such systems, the main controller unit receives the wireless power and provides supply voltages for its own circuits and the circuits in the connected sub-units. Automatic conversion ratio reconfiguration, according to the input voltage level, achieves high conversion efficiency over a wide input voltage range. In addition, the load selection switches provide more reconfigurability by selecting the most suitable converter for each output voltage, reducing the number of flying capacitors required for the reconfigurable conversion ratios to cover a wide input range. These selection switches are reused for regulating output voltages at desired levels, minimizing the overhead for regulation. The implemented IC achieves a conversion efficiency of 51% to 79% over a wide input range of 1.1 V to 4 V with 3-mV output ripple for a load current of 60 mA while providing multiple outputs (2 regulated, 2 unregulated) for multi-unit operation and the peak efficiency is 95.7% for unregulated outputs.
Efficient Feature-Aware Hybrid Model of Deep Learning Architectures for Speech Emotion Recognition

Robust automatic speech emotional-speech recognition architectures based on hybrid convolutional neural networks (CNN) and feedforward deep neural networks are proposed and named in this paper as: BFN, CNA, and HBN. BFN is a combination between bag-of-Audio-word (BoAW) and feedforward deep neural network, CNA based on CNN, finally, HBN is hybrid architecture between BFN and CNA. Overall accuracy is achieved by leveraging Mel-frequency cepstral coefficient features and bag-of-acoustic-words to feed the network, resulting in promising classification performance. In addition, the concatenated output from the proposed hybrid networks is fed into a softmax layer to produce a probability distribution over categorical classifications for speech recognition. The three proposed models are trained on eight emotional classes from the Ryerson Audio-Visual Database of Emotional Speech and Song audio (RAVDESS) dataset. Our proposed models achieved overall precision between 81.5% and 85.5% and overall accuracy between 80.6% and 84.5%, hence outperforming state-of-the-art models using the same dataset.
Disturbance Observer-Based Cooperative Learning Tracking Control for Multi-Manipulators

This paper studies the disturbance observer-based cooperative learning tracking control for a homogenous multi-robot manipulator system. Firstly, in the ideal environment, adaptive neural network (NN) controllers are constructed based on distributed cooperative learning (DCL) to obtain the knowledge of unknown manipulator system dynamics. Then, for similar tasks in the practical environment full of disturbances, in order to avoid the re-adaption of NN weights, the knowledge learned in the ideal environment is used to construct the novel controllers based on disturbance observer, which effectively enhances the robustness of the proposed controllers. The boundedness of all signals in the closed-loop system is guaranteed by employing the proposed control scheme, and the tracking error of each manipulator eventually converge to a small neighborhood of the origin. Finally, a numerical example is given to verify the effectiveness of the proposed control strategy.
Study the Impact of COVID-19 on Twitter Users with respect to Social Isolation

The COVID-19 pandemic is a major public health issue that has affected millions of people in many different manners. This research proposes a novel approach to study and monitor this pandemic's impact on Twitter users concerning Social Isolation. Various sets of analyses is conducted to identify potential correlations between tweets related to social isolation, people's movement trends, and their emotional status. In this research, multiple datasets are used, ranging from our own collected dataset to a huge COVID-19 twitter dataset with over 140 million tweets and from the Google mobility report to the World Health Organization dataset. A couple of cloud-based services are used to analyze this data and extract patterns and insights from them. We believe the results of this research can be used as a public health indicator to anticipate the possibility of social isolation and design the health policies accordingly.
Highly Stabilized Nucleotide‐Capped Small Gold Nanoparticles with Tunable Size

a radiation ( k =0.154 nm) operated at 40 kV and 40 mA. Zeta potentials of AuNPs were measured at room temperature by using a ZetaPlus (Brookhaven Instruments Corporation). The report-ed values were based on 10 measurements with 15 cycles for each sample.
Alzheimer Disease Early Detection Using Convolutional Neural Networks

Alzheimer’s disease is the extremely popular cause of dementia that causes memory loss. People who have Alzheimer’s disease suffer from a disorder in neurodegenerative which leads to loss in many brain functions. Nowadays researchers prove that early diagnosis of the disease is the most crucial aspect to enhance the care of patients’ lives and enhance treatment. Traditional approaches for diagnosis of Alzheimer’s disease (AD) suffers from long time with lack both efficiency and the time it takes for learning and training. Lately, deep-learning-based approaches have been considered for the classification of neuroimaging data correlated to AD. In this paper, we study the use of the Convolutional Neural Networks (CNN) in AD early detection, VGG-16 trained on our datasets is used to make feature extractions for the classification process. Experimental work explains the effectiveness of the proposed approach.
Prediction algorithm for micro-loudspeaker diaphragm displacement based on PG-LSTM with 44% MSE reduction

In this paper, the author proposes to use PG-LSTM (physics guided long short-term memory neural network) to model the circuit and mechanical structure of the micro-loudspeaker to improve the accuracy of the diaphragm displacement prediction. Compared with conventional physical model, the PG-LSTM model takes into account the influence of temperature on the loudspeaker system, and reduces the displacement prediction errors caused by system nonlinear factors when the input signal amplitude is large. The experimental results show that the mean-squared-error and maxim-absolute-error of the proposed model is reduced by 44% and 53% compared to the conventional physical model respectively.
A Nove1 24-Pu1se Star Rectifier Using Simple Passive Injection Method at DC Link

This paper presents a novel 24-pulse uncontrolled star rectifier for low voltage and high current industrial applications. The proposed 24-pulse star rectifier consists of 12-pulse star rectifier and passive injection circuit. Specially tapped interphase transformer (STIPT) with two diodes constitutes the passive injection circuit. Passive injection circuit provides circulating harmonic current through the load which modify the output voltage and input line currents of 12-pulse star rectifier. Proposed model operates as a 24-pulse rectifier as tap ratio of the STIPT is chosen optimally. At optimal tap ratio, total harmonic distortion (THD) of the input line current is about 7.32% and that of input line current with source impedance and leakage reactance is about 4.67%. Basic performance of the proposed 24-pulse star rectifier is validated by proper theoretical analysis and simulation results of MATLAB/Simulink.
RELATIVE RESPONSE OF FORAGE AND SEED YIELD OF ALFALFA TO SULFUR, PHOSPHORUS, AND POTASSIUM FERTILIZATION

A field experiment was conducted from 2000 to 2007 on three-year old alfalfa stand near Star City in northeastern Saskatchewan to determine the influence of balanced application of sulfur (S), phosphorus (P), or potassium (K) fertilizers on forage dry matter yield (DMY) and seed yield, protein concentration (PC) in forage, concentration and uptake of total nitrogen (N), P, K, S, and boron (B) in forage and seed, and residual soil extractable P, exchangeable K and sulfate-S. Appropriate application of S, P, or K fertilizer nutrients was required to obtain optimum yield, PC, and nutrient concentration and uptake, especially when alfalfa was grown for hay production. Yield response to fertilization was much more frequent and much higher when alfalfa was managed as hay compared to when managed for seed production. The results suggest the importance of proper fertilization in increasing longevity of alfalfa stands. Under both hay and seed plots, there was only a small increase in residual sulfate-S from S fertilization, but significant accumulation of extractable P mainly in the 0–15 soil layer from P application. There was relatively higher concentration of exchangeable K in soil in the seed plots than in hay plots. In hay plots, concentrations of residual exchangeable K in soil were negatively related to DMY, especially in 2007. The findings suggest that when a soil is testing low (or deficient) in a nutrient and alfalfa growth is reduced, then alfalfa producers should consider applying fertilizers to supply adequate amounts of nutrients that are lacking in the soil, especially for optimum forage production. However, it is still difficult to predict accurately if a profitable alfalfa seed yield response to fertilization would occur, particularly when soils are testing marginal in some nutrient levels and yields are negatively affected by abnormal weather conditions (drought soil moisture conditions reducing plant growth, wet, cloudy and cool weather conditions decreasing pollination activity and late summer and/or early autumn frost damage to seed formation) that often occur in the growing season in this region.
VM Introspection-based Allowlisting for IaaS

Cloud computing has become the main backend of the IT infrastructure as it provides ubiquitous and on-demand computing to serve to a wide range of users including end-users and high-performance demanding agencies. The users can allocate and free resources allocated for their Virtual Machines (VMs) as needed. However, with the rapid growth of interest in cloud computing systems, several issues have arisen especially in the domain of cybersecurity. It is a known fact that not only the malicious users can freely allocate VMs, but also they can infect victims’ VMs to run their own tools that include cryptocurrency mining, ransomware, or cyberattacks against others. Even though there exist intrusion detection systems (IDS), running an IDS on every VM can be a costly process and it would require fine configuration that only a small subset of the cloud users are knowledgeable about. Therefore, to overcome this challenge, in this paper we present a VM introspection based allowlisting method to be deployed and managed directly by the cloud providers to check if there are any malicious software running on the VMs with minimum user intervention. Our middleware monitors the processes and if it detects unknown events, it will notify the users and/or can take action as needed.
A Millimeter-Wave Differential Filtering Dual-Patch Antenna Based on Coupling Power Divider Feeding

In this paper, a millimeter-wave (mmWave) differential filtering dual-patch antenna with filtering response is proposed to achieve filtering response by two E-shaped patches and a filtering power divider (FPD). Two pairs of slots on each E-shaped patch produce radiation nulls near the operating band. Moreover, square slots and parasitic patches are added to the patches, which results in wider operating bandwidth. An example of the proposed antenna is designed and simulated with the 10-dB bandwidth of 8.92%, the 15-dB bandwidth of 6.25%, and the maximum gain of 6.92 dBi. The proposed antenna provides a new mean to implement mmWave filtering communications, which would be attractive for future mmWave wireless systems used for 5G communications and other applications.
An Early Fault Diagnosis Method of Rolling Element Bearings Based on MED, DFA, and Improved KNN

Rolling bearings are the key component of rotating machinery. In the early stage of bearing fault, the faint feature is susceptible to environmental noise. Therefore, weak fault characteristics are difficult to be extracted. An early fault diagnosis method based on minimum entropy deconvolution (MED), detrended fluctuation analysis (DFA), and improved K-nearest neighbor (IKNN) is proposed in this paper. First, the MED filter is employed for the enhancement of the fault components which are drowned in the time-domain vibration signal. The DFA method is then used for the extraction of fault features from the enhanced signals. Finally, the proposed IKNN algorithm is applied as the classifier to identify the fault types. Three kinds of early failures occurred in the inner race, cage, and outer race are performed on the bearing test rig. The experimental results show that the average classification accuracy of the proposed diagnosis method can reach 97.5%.
Proposal of device control method based on consensus building using reinforcement learning

Various information is collected from IoT devices through the network. As such device becomes more familiar to the user, services are required to consider the influence of user. However, it is difficult to set the parameters of actuators that build consensus among all users in an environment where people with various preferences coexist. The conventional method minimizes the power consumption under the constraints of the user stress. However, this method has a problem that the calculation overhead is increased as the number of devices and users is increased. In this study, we propose a device control method based on consensus building with reinforcement learning. In the proposed method, the state is reduced by applying reinforcement learning for reducing the calculation overhead. As a result of evaluation, we clarified that our method obtains the device parameters that improve the reward by 1.5 times compared with the conventional method. Moreover, we also clarified that a reward value of 98.6% can be achieved compared to the optimum value.
A $186\mu \mathrm{W}$ Glucose Monitoring SoC using Near-Infrared Photoplethysmography

A glucose monitoring SoC based on near-infrared Photoplethysmography (PPG) is presented. It integrates fully differential AFE with nonlinear support-vector-machine regression (NSVMR). The AFE utilizes chopper to reduce input-referred current noise by 57%, and trans-impedance amplifier input impedance by 90%, thus allows 115dB dynamic range. The NSVMR is realized using the piecewise linear floating-point exponential, which decreases the area by 27% compared to conventional implementations. The 6mm2 SoC in 0.18um CMOS consumes $186 \mu \mathrm{W}$ and reduces the mean absolute relative difference (mARD) by 30% verified on 200 subjects.
A 28 GHz and 38 GHz Dual-Band LNA Using Gain Peaking Technique for 5G Wireless Systems in 22 nm FD-SOI CMOS

This paper presents a high-gain dual-band low noise amplifier (LNA) for 5G wireless systems. The LNA is implemented in 22nm FD-SOI CMOS technology and operates simultaneously in the 28 GHz and 38 GHz millimeter-wave 5G bands. The circuit consists of three cascaded cascode amplifier stages. To obtain a dual-band response, gain peaking technique was implemented. Single-band matching networks were used to match the circuit input and inter-stage at different target frequencies. A dual-band matching network at the output ensures low return losses at both frequencies of interest. For a power consumption of 13. 6mW from a 1. 6V supply, the LNA provides a peak gain of 25. 1dB and a minimum noise Figure of 3.6 dB. The measured gain at 28 GHz and 38 GHz is greater than 22 dB. For a competitive power consumption, the LNA compares well against previously reported designs by showing one of the highest measured gain, gain-bandwidth product (GBW) and comparable noise figure.
Broadband Power Amplifiers Behavioral Modeling Method Based on Instantaneous Input Power

This paper presents a novel Augmented Memory Polynomial (AMP) model for the broadband radio frequency high power amplifiers behavioral modeling and digital predistortion. The proposed model consists of a weighted memoryless sub model and a weighted memory sub model. The weighted memoryless sub model involving a weighting, polynomial error signal, and de-weighting parts is used for predicting the strong static nonlinear behaviors of the power amplifiers. The weighted memory sub model is used for the dynamic memory nonlinearity by introducing an instantaneous power dependent weight function into the static and dynamic terms of the conventional memory polynomial (CMP) model. The performance of the proposed model is assessed in terms of in-band and out-of-band errors, such as normalized mean square error (NSME) and adjacent channel power ratio (ACPR). Compared with the CMP, the weighted memory polynomial (WMP) is complex in computation due to the numbers of model coefficient. Furthermore, the proposed method is applied to the digital predistortion so as to cancel the nonlinearity of the power amplifier. The experimental results show that the proposed for modelling the power amplifier and applying the digital predistortion is feasible and effective because it can result in up to 10dB improvement in the ACPR for the same number of coefficients.
A 8.4Gb/s Low Power Transmitter with 1.66 pJ/b using 40:1 Serializer for DisplayPort Interface

This paper presents the design of the 8.4Gb/s transmitter with a two-tap feed-forward equalizer (FFE) and a 40:1 serializer. The transmitter includes an all-digital phased-locked-loop (ADPLL), a pre-driver and a driver. The simple architecture of the 5:1 serializer achieves low-power consumption by eliminating delay line buffers used to secure timing margin and the selection generator in the conventional 5:1 serializers. The prototype is fabricated in a 40-nm CMOS technology. It offers 72.5-ps eye width, which is 61% of the unit interval and exhibits energy efficiency of 1.66 pJ/bit at 8.4Gb/s.