Miracle-dz/UniFAR-FTUs huggingface dataset:
Binary Search and Sectional Theory-Based Maximum Power Point Tracking Algorithms for Solar Photovoltaic Systems

This paper proposes sectional theory and binary search-based maximum power point tracking (MPPT) algorithms for solar photovoltaic (PV) systems. The maximum power of the solar PV under various environmental conditions are obtained by employing these algorithms. The corresponding voltage of the maximum power is computed in every step with the proposed logic, which moves the operation point closer to the maximum power point. The proposed methods are validated using the MATLAB/SIMULINK software tool. The results are bench-marked against the conventional adaptive step-based perturb and observe method. The proposed algorithms improve the performance in terms of convergence rate and reduced steady state oscillations. The results demonstrate that the sectional theorem-based algorithms are either less accurate or has slower convergence rate while the binary search-based MPPT technique is the most efficient among the algorithms tested.
A promising novel biomarker for early-onset preeclampsia: soluble trigger receptor expressed on myeloid cells-1 (sTREM-1)

Abstract Background We aimed to explore TREM-1 activation in pregnant women who has preeclampsia through the measurement of its soluble form sTREM. Methods A prospective cohort study was conducted. Participants were recruited from antenatal clinic between 1 May 2019 and 31 August 2019, and they all provided written informed consent for participation. Women between 18 and 42 years of age who were diagnosed with early or late-onset preeclampsia (LOP) were offered participation if they did not have any known systemic disease (chronic hypertension, diabetes, hypothyroidism, chronic renal-liver diseases, etc.); autoimmune disorders; multiple pregnancies; presence of fetal structural and chromosomal anomalies; placenta previa; cholestasis of pregnancy; preterm delivery; evidence of chronic and active infection. The primary outcome of the study was to assess any difference between groups in terms of the diagnostic value of sTREM level. Results A total of 80 patients were enrolled; proven early-onset preeclampsia (EOP) (n = 20), LOP (n = 30), and control (n = 30) groups. There was no significant difference among the groups in terms of age and BMI. Mean gestational age at diagnosis of EOP; 30 ± 1.9 and LOP; 34.7 ± 1.9 weeks gestation. The mean sTREM level was 160.130 ± 1.65 pg/ml in the EOP group, 119.337 ± 2.04 pg/ml in LOP group, and 87.764 ± 1.69 pg/ml in the control group. According to subgroup analysis, sTREM levels were significantly higher in EOP group than control group. Conclusions sTREM might be a promising biomarker for early detection of EOP. However, future studies are necessary to confirm this hypothesis.
Evaluation of Contrast to Noise Ratio of Parametric Images of Regularized Estimates of Quantitative Ultrasound

The granular appearance of B-mode image emanates from scattering particles, rendering visual assessment of B-mode image difficult. However, the analysis of echo signals that produce B-mode image speckle can disclose important information about the physical characteristic of the tissue. Quantitative ultrasound investigates the frequency content of backscattered echo signals to provide estimates of acoustic properties of tissue. Regularized estimation leads to more accurate and precise estimates of such acoustic properties. In this study, we investigate whether parametric images of regularized estimates of the acoustic concentration can provide better conspicuity of high contrast objects than conventional B-mode images. To this end, we apply regularized estimation of acoustic concentration using Dynamic Programming (DP) to data acquired from a Gammex 410SCG phantom. The phantom contains three inclusions with different echogenicities, which are created from different concentration of scatterers. Conspicuity is quantified in terms of the inclusion contrast to noise ratio (CNR) and border resolution. Our results demonstrate that using regularized QUS, the CNR of parametric images of the acoustic concentration was substantially higher CNR than those of conventional B-mode images, at the expense of border resolution.
Fast Scanning Method for Measuring Material Homogeneity using the Line-Focus-Beam Ultrasonic-Material-Characterization System

A new method for measuring material homogeneity using fast scanning technique was proposed and demonstrated with the line-focus-beam ultrasonic-material-characterization (LFB-UMC) system. Similar velocity profiles of leaky surface acoustic wave (LSAW) for a Ca3Ta(Ga0.75Al0.25)3Si2O14[CTGAS] single crystal specimen were successfully obtained by both of the new V(x) and the conventional V(z) methods. We have verified the new V(x) method was 56 times faster than the conventional V(z) method. Although the V(z) method is superior to the V(x) method in a viewpoint of measurement accuracy, the V(x) method has great advantage enabling us to quickly evaluate material homogeneity over the wide area of specimen.
A Design of DSP, CPU architecture on FPGA for secure routers

This paper presents the design of a DSP chip including the design of CPU architecture, instruction set, bus architecture, memory interface, and peripherals. CPU named as P10 in this article has 05 stages: Fetch, Decode, Read, Execute, Write (08 phases independent). In addition, a coprocessor (Floating Point Unit) that performs floating point 32-bit is also integrated into our design. The bus used in our work includes three protocols: APB, AHB, and AXI. Furthermore, memory M10 have up to 4GB for data space and 4MB for program space. Testing environment and secure router hardware are designed and built to verify our design on FPGA, and ASIC flow with library 65nm TSMC technology.
Comparative Analysis of Various Machine Learning Techniques for Epileptic Seizures Detection and Prediction Using EEG Data

Epileptic seizures occur as a result of functional brain dysfunction and can affect the health of the patient. Prediction of epileptic seizures before the onset is beneficial for the prevention of seizures through medication. Electroencephalograms (EEG) signals are used to predict epileptic seizures using machine learning techniques and feature extractions. Nevertheless, the pre-processing of EEG signals for noise removal and extraction of features are two significant problems that have an adverse effect on both anticipation time and true positive prediction performance. Considering this, the proposed model will provide remarkable methods for both pre-processing and extraction of features. The proposed model detects various brain states and accounts for both epileptic seizures detection and prediction. Using the EEG CHB-MIT dataset, the support vector machine (SVM) model was trained, tested, and compared, having a best true positive percentage of 91% for a single patient and 82% for multiple patients. The SVM algorithm was also compared to other machine learning algorithms such as K-Nearest Neighbors (KNN) proving to be more efficient with a true positive percentage of 82% than KNN with 80%.
Road Condition Monitoring with Use of MEMS Based Unit

The paper deals with the design and use of smart unit for real-time road condition monitoring. The basis is three-axis accelerometer, gyroscope and magnetometer based on MEMS technology, which provides its data to the main microcontroller. As a communication interface for data providing, the high-speed CAN bus was used. The reason was an easier way to implement in the car and achieve a higher frequency of data provision. The result of this work is the determination of the critical sections of the measured route.
A MRAS Observer Based Sensorless Control of Doubly-Fed Reluctance Wind Turbine Generators

A new model reference adaptive system (MRAS) based estimation technique for vector control of a brushlesss doubly fed reluctance generator (BDFRG) without a shaft position sensor is proposed. The rotor speed is being precisely observed in a closed-loop fashion through an iterative error eradication process between the measured and estimated secondary current angles in a stationary frame. Contrary to the existing MRAS designs reported in the BDFRG literature, the reference model only utilises direct measurements of the secondary currents with-out any machine parameters. Furthermore, the current estimates coming from the adaptive model are obtained using the measured grid voltages and currents, which has provided prospects for much higher accuracy and superior overall performance. The realistic simulations and the accompanying parameter sensitivity studies have shown the great controller potential for typical operating conditions of wind turbines as the main target application.
PWM Step-Up Converter Integrating Differential Power Processing Converter to Enhance Energy Yield of Partially-Shaded Photovoltaic Panels

Partial shading on a photovoltaic (PV) panel consisting of multiple substrings is known to trigger a significant reduction in power yield and the occurrence of multiple maximum power points (MPPs). Although differential power processing (DPP) converters can preclude the negative influences of partial shading, an additional DPP converter is separately required in addition to an existing step-up converter for panel control, resulting in increased circuit volume and system complexity. This paper proposes a novel integrated converter that realizes system simplification and circuit miniaturization by integrating a PWM step-up converter and a DPP converter. Laboratory and field tests were performed emulating partial shading and characteristic mismatch conditions. A local MPP successfully disappeared, and the extractable maximum power increased while boosting the panel voltage, demonstrating the efficacy of the proposed integrated converter.
Office Appliances Identification and Monitoring using Deep Leaning based Energy Disaggregation for Smart Buildings

Analysis of electrical energy metering profiles has experienced a substantial increase of research activity in recent years. This smart metering is a tool for monitoring energy usage and users’ behaviors as a pre-requisite for substantial energy savings. Instead of having a sensor at each appliance, non-Intrusive Load Monitoring (NILM) provides a cheaper solution by disaggregating the load data from a single meter using digital signal processing. Different algorithms have been successfully applied to a variety of load scenarios. Load data for small office appliances is available in the BLOND data set (Building-Level Office eNvironment Dataset) such as laptops, computer monitors, etc. The potential energy saving of each small appliance cannot be neglected, particularly in large companies/institutesIn this paper, a recurrent neural network (RNN) with long-short term memory (LSTM) is designed, trained, and validated for NILM on small power office equipment provided in the BLOND data set. A comparison to combinatorial optimization and factorial hidden Markov models using five metrics for performance testing shows good results for the proposed RNN.
Redundancy Resolution based Trajectory Generation for Dual-Arm Aerial Manipulators via Online Model Predictive Control

This paper presents a nonlinear model predictive control-based trajectory generation with redundancy resolution strategy for a dual-arm aerial manipulator. The proposed approach continuously re-plans the aerial robot motion to track the position of the dual-arm end-effectors, while satisfying other constraints such as avoiding the obstacles and limits of the motion variables. In addition, the minimum manipulability measure is set to ensure a smooth motion and avoid singular configurations of the aerial robot. The performance of the developed framework is shown via simulations and real-time experimental flight tests, involving the dual-arm aerial manipulator performing non-trivial multi-task missions, which require the coordination of both arms, while keeping the configuration of the aerial robot within imposed bounds and preventing collisions with obstacles.
Turing Meets Shannon: Computable Sampling Type Reconstruction With Error Control

The conversion of analog signals into digital signals and vice versa, performed by sampling and interpolation, respectively, is an essential operation in signal processing. When digital computers are used to compute the analog signals, it is important to effectively control the approximation error. In this paper we analyze the computability, i.e., the effective approximation of bandlimited signals in the Bernstein spaces <inline-formula><tex-math notation="LaTeX">$\mathcal {B}_{\pi }^p$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$1 \leq p < \infty$</tex-math></inline-formula>, and of the corresponding discrete-time signals that are obtained by sampling. We show that for <inline-formula><tex-math notation="LaTeX">$1< p< \infty$</tex-math></inline-formula>, computability of the discrete-time signal implies computability of the continuous-time signal. For <inline-formula><tex-math notation="LaTeX">$p=1$</tex-math></inline-formula> this correspondence no longer holds. Further, we give a necessary and sufficient condition for computability and show that the Shannon sampling series provides a canonical approximation algorithm for <inline-formula><tex-math notation="LaTeX">$p>1$</tex-math></inline-formula>. We discuss BIBO stable LTI systems and the time-domain concentration behavior of bandlimited signals as applications.
Stability of Conductive Carbon Additives for High-voltage Li-ion Battery Cathodes

Conductive carbon additives are important constituents of the current state-of-the-art Li-ion battery cathodes, as the traditional active cathode materials are characterized by too low electronic conductivities. In high-voltage Li-ion batteries, these additives are subject for anion intercalation and electrolyte oxidation, which might cause changes in the conductive carbon network in the cathode, and hence the overall cycling performance of the electrode. This thesis has focused on study the stability of three types of carbon additives operating at high voltages. Materials included were two conventional types of conductive additives, graphite, KS6, and carbon black, Super P Li, both provided from TIMCAL. In addition, a multilayer graphene powder, Graphene AO-2, provided from Graphene Supermarket has been investigated. The powder properties, size, shape and structure, were studied in a scanning electron microscope and by powder X-ray diffraction. Electrodes from these materials were cycled galvanostatically and with cyclic voltammetry to reveal their high-voltage behaviour, with respect to the two above mentioned electrochemical processes. More detailed investigations of anion intercalation in KS6 and Graphene AO-2 were conducted by in situ X-ray diffraction measurements and scanning electron microscopy. For cycling in 30:70 vol% EC/DMC 1 M LiPF6 to a voltage of 4.7 V vs. Li+/Li, the results showed that Super P Li can be considered as the most stable conductive additive. At 4.7 V vs. Li+/Li, both KS6 and Graphene AO-2 are electrochemically active, while Graphene AO-2 displays a more reversible behavior, and are more stable than KS6. For even higher operation potential, 5.0 V vs. Li+/Li, Graphene AO-2 showed the least stable behavior of the three materials, due to large degree of electrolyte oxidation, unstable anion intercalation upon continuous cycling, and the suggestions of structural degradation of the electrode. However, a small increase in the electrolyte stability window was shown with Graphene AO-2 when changing to a more viscous electrolyte (1:1 vol% EC/DMC 1 M LiPF6) or adding an anion receptor (tris(hexafluoroisopropyl)borate) to the electrolyte. The anion intercalation in KS6 was observed to form staged phases, starting at 4.83 V vs. Li+/Li. The electrode structure was suggested to be affected in a smaller extent compared to Graphene AO-2. In addition, less electrolyte was oxidized at the KS6 electrode surface, compared to the two other materials. However, the film formed on the Super P Li electrodes did not seem to affect the carbon negatively, because stable anion intercalation was observed upon continuous cycling. Indicating that Super P Li was not strongly affected by the intercalation process.
Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

Understanding, modeling, and predicting student performance in higher education poses significant challenges concerning the design of accurate and robust diagnostic models. While numerous studies attempted to develop intelligent classifiers for anticipating student achievement, they overlooked the importance of identifying the key factors that lead to the achieved performance. Such identification is essential to empower program leaders to recognize the strengths and weaknesses of their academic programs, and thereby take the necessary corrective interventions to ameliorate student achievements. To this end, our paper contributes, firstly, a hybrid regression model that optimizes the prediction accuracy of student academic performance, measured as future grades in different courses, and, secondly, an optimized multi-label classifier that predicts the qualitative values for the influence of various factors associated with the obtained student performance. The prediction of student performance is produced by combining three dynamically weighted techniques, namely collaborative filtering, fuzzy set rules, and Lasso linear regression. However, the multi-label prediction of the influential factors is generated using an optimized self-organizing map. We empirically investigate and demonstrate the effectiveness of our entire approach on seven publicly available and varying datasets. The experimental results show considerable improvements compared to single baseline models (e.g. linear regression, matrix factorization), demonstrating the practicality of the proposed approach in pinpointing multiple factors impacting student performance. As future works, this research emphasizes the need to predict the student attainment of learning outcomes.
Analytic Solutions for Minimum Conduction Loss Modulation of Dual Active Bridge Converters Including Frequency Variation

This paper presents analytic solutions for the calculation of an optimal modulation scheme including frequency variation for a bidirectional single phase dual active bridge (DAB) dc-dc converter used for charging high-voltage batteries of electric vehicles. The proposed modulation scheme facilitates the minimum conduction loss operation of the DAB converter. The losses can be reduced significantly, especially for light- and medium-load conditions. Analytic expressions are derived for a direct calculation of the optimal control parameters. These expressions are verified with a numerical simulation. Moreover, a significant reduction of the necessary computational time can be achieved for the analytic determination.
Boost Type Two-Stage Multi-Input High Frequency Link’s Inverter with Multiple Windings Simultaneous Power Supply

The single new energy power supply method has defects such as instability and discontinuousness. In order to improve the flexibility and stability of the power generation system and achieve the preferential utilization of new energy, this paper proposes a Boost type two-stage multi-input high frequency link’s inverter with multi-winding simultaneous power supply, and the circuit topology, energy management control strategy (EMCS), high frequency switching process and active clamp circuit design of the inverter are studied and important conclusions are obtained. The circuit topology is composed of a two-level cascade of a multi-input Boost DC-DC converter and a Buck inverter. The front stage adopts the maximum power output phase-shifting pulse width modulation (PWM) control strategy with power limit. The rear stage adopts the single- polarity twice-frequency sinusoidal pulse width modulation (SPWM) control strategy. The maximum power output of multiple input sources is adjusted by controlling the DC bus voltage. The designed and developed 1kVA dual-input inverter prototype has excellent performance such as high frequency isolation between output and input and multiple input sources, small volume and weight, wide duty cycle adjustment range, high conversion efficiency, etc., which verifies the feasibility of circuit topology and EMCS.
Ultrasound Signal Detection with Multi-bounce Laser Microphone

The multi-bounce laser microphone utilizes optical methods to detect the displacement of a gold-covered thin film diaphragm caused by ultrasound signal pressure waves. This sensitive all-optical sensing technique provides new opportunities for advanced ultrasound imaging as it is expected to achieve a higher detection signal-to-noise ratio (SNR) in a broader spectrum, as compared to conventional ultrasonic transducers. The technique does not involve signal time-averaging and the realtime enhancement in detection SNR stems from the amplification of signal strength due to multiple bouncing off the diaphragm. The system was previously developed for detecting acoustic signatures generated by explosives and were limited to lower than 10 kHz in frequency. To demonstrate its feasibility for biomedical imaging applications, preliminary experiments were conducted to show high fidelity detection of ultrasound waves with frequencies ranging from 100 kHz to in excess of 1 MHz. Experimental results are also presented in this work demonstrating the improved detection sensitivity of the multi-bounce laser microphone in detecting ultrasound signals when compared with a commercial Fabry-Perot type optical hydrophone. Furthermore, we also applied the multi-bounce laser microphone to detect photoacoustic signatures emitted by India ink when a LED bar is used as the excitation source without signal averaging.
Metagenomic analysis reveals large potential for carbon, nitrogen and sulfur cycling in coastal methanic sediments of the Bothnian Sea

The Bothnian Sea is an oligotrophic brackish basin characterized by low salinity and high concentrations of reactive iron, methane and ammonium in the sediments potentially enabling an intricate microbial network. Therefore, we analyzed and compared biogeochemical and microbial profiles at one offshore and two near coastal sites in the Bothnian Sea. 16S rRNA amplicon sequence analysis revealed stratification of both bacterial and archaeal taxa in accordance with the geochemical gradients of iron, sulfate and methane. The communities at the two near coastal sites were more similar to each other than that at the offshore site located at a greater water depth. To obtain insights into the metabolic networks within the iron-rich methanic sediment layer located below the sulfate-methane transition zone (SMTZ), we performed metagenomic sequencing of sediment-derived DNA. Genome bins retrieved from the most abundant bacterial and archaeal community members revealed a broad potential for respiratory sulfur metabolism via partially reduced sulfur species. Nitrogen cycling was dominated by reductive processes via a truncated denitrification pathway encoded exclusively by bacterial lineages. Gene-centric fermentative metabolism analysis indicated the central role of acetate, formate, alcohols and hydrogen in the analyzed anaerobic sediment. Methanogenic/-trophic pathways were dominated by Methanosaetaceae, Methanosarcinaceae, Methanomassiliicoccaceae, Methanoregulaceae and ANME-2 archaea. Thorarchaeota and Bathyarchaeota encoded pathways for acetogenesis. Our results indicate flexible metabolic capabilities of core community bacterial and archaeal taxa, which can adapt to changing redox conditions, and with a spatial distribution in Bothnian Sea sediments that is likely governed by the quality of available organic substrates.