Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Piezoelectric Sensor for Forcemyography Application

Forcemyography (FMG) is a technique that provides information on muscle activity by measuring the variation of muscle cross-section during contraction. FMG is a viable alternative to Electromyography (EMG) for monitoring muscle activity in applications such as human machine interfaces. Typically, FMG makes use of Force Sensitive Resistor (FSR) sensors. A new piezoelectric sensor based on common and inexpensive lead zirconate titanate (PZT) disk is presented. To evaluate its operation, signals from the new sensor, an FSR sensor and EMG electrodes were simultaneously acquired on the biceps brachii of 4 healthy subjects while performing about 50 isometric contractions of different intensity and duration. To estimate the level of muscle activity, the EMG signal was processed by extracting its linear envelope (EMG-LE). Each muscle contraction was then segmented. The similarity between the three signals was assessed via their normalized cross-correlation index (NCCI). The NCCI computed between the FMG signals and the EMG-LE and averaged across all the contractions scored 0.89 (SD: 0.042) for the PZT sensor and 0.88 (SD: 0.050) for the FSR sensor. Instead, the average NCCI computed between the two FMG signals scored 0.97 (SD: 0.020). The delay between the contraction onsets in the FMG signals and those in the EMG-LE was about 20 ms on average. The results confirm the good performance obtained by the new sensor in monitoring muscular activity, an excellent degree of equivalence between the signal provided by the PZT and the FSR sensors, and the good correspondence with the information provided by the EMG-LE.
Learning Unknown Lagrange Dynamical Systems with Guaranteed Persistency of Excitation

In this paper, we present a methodology that ensures a priori that all possible unknown dynamics of the system within a compact set of operation will be excited. A controller is used to make sure that the system with unknown dynamics will follow the reference trajectory and Radial Basis Function (RBF) neural networks are employed to estimate the unknown nonlinearities. The persistency of excitation condition is guaranteed as a prerequisite to achieve accurate estimation of the unknown nonlinear terms and efficient learning. A simulation example clarifies the proposed approach and verifies the aforementioned assertions.
Generation of Plane Waves in the Nearfield Region in a Restricted Environment

A wideband dual-polarized plane wave generator (PWG) based on an adaptive least squares (LS) for plane wave synthesis in a restricted environment is presented. Here we achieve plane wave synthesis at short distances with precisionlimited weighting coefficients by applying an iteration limiting method to LS. The PWG performance is validated via numerical simulation. The results show that an adaptive LS provides weighting coefficients for synthesizing a stable plane wave even in a restricted environment compared to the general LS. This indicates that the proposed method is applicable to various PWG systems and realistic environments.
EpiMCBN: A Kind of Epistasis Mining Approach Using MCMC Sampling Optimizing Bayesian Network

Proposing a more effective and accurate epistatic loci detection method is of great significance in improving crop quality, disease treatment, etc. Due to the characteristics of high accuracy and processing non-linear relationship, Bayesian network (BN) has been widely used in constructing the network of SNPs and phenotypes and thus to mine epistasis. However, the shortcoming of BN is that the search space is too large and unable to process large-scale SNPs. In this work, we propose a kind of epistasis mining method using Markov Chain Monte Carlo (MCMC) sampling optimizing Bayesian network (EpiMCBN). Firstly, we use the space of node order composed of SNPs and phenotype to replace the space of network structure. Then MCMC algorithm is used to do sampling to generate multiple different initial orders in linear space or partial space. We use Markov state transition matrix to transfer the initial samples along the Markov chain, thus obtaining multiple order samples. Then we use the $\alpha$-BICBN scoring function to score the Bayesian networks corresponding to these node orders. Through estimating the probability of edge occurrence in the Bayesian networks, we get an approximate Bayesian network of SNPs and phenotype, then obtain the epistatic loci affecting phenotype. Finally, we compare EpiMCBN with the current popular epistasis mining algorithms using both simulated and real age-related macular disease (AMD) datasets. Experiment results show that EpiMCBN has better epistasis detection accuracy, lower false positive rate, and higher F1-score compared to other methods. Availability and implementation: Source code and dataset are available at: http://122.205. 95.139/EpiMCBN/.
QRONOS : Towards Quality-Aware Responsive Real-Time Control Systems

A key design goal of safety-critical control systems is the verifiable compliance with a specific quality objective in the sense of the quality of control. Corresponding to these requirements, the underlying real-time operating system has to provide resources and a certain quality of service, mainly in the form of timing guarantees. For the design of efficient real-time control systems, considering only the quality of service is insufficient as it is firmly intertwined with the quality of control: First of all, the actual timing has a significant and nontrivial influence on the quality of control. Vice versa, the temporal precision required to provide a certain quality of control may vary considerably with environmental situation and disturbance. Consequently, qualityof-service requirements are not fixed but may vary depending on the execution context. We present our ongoing work on quality-aware adaptive realtime control systems, addressing three challenges: evaluating quality of control under consideration of varying timing, static worst-case verification, and quality-aware scheduling at runtime.
Advanced Systems Integration of 540 KVA Inverter and 1.86 MWh Battery Energy Storage System for Microgrid Application: A Case Study

This paper discusses the realization of advanced systems integration that has 540 KVA three-phase bidirectional inverter and 1863.68 kWh lithium-ion battery energy storage system (BESS) at Florida Solar Energy Center (FSEC) with a partnership of University of Central Florida (UCF) and A.F. Mensah Inc. The purpose of this paper is to give a case study and an overview of advanced systems integration by providing their test results. It enables the reader to understand what a microgrid is in a real application. After explaining the overview of the system, the real energy consumption, the reduced energy consumption, the battery testing data, the voltage, and current waveforms are demonstrated and analyzed by determining its total harmonic distortion (THD) values and simulating its results in different power management scenarios. The system is now utilized at FSEC without the PV Panels integration. The PV integrated system scenario is explained by providing theoretical and simulation results. As a result, the BESS capacity should be "4 hour" times of the PV panel power, and the THD level is determined 0.07 when 200-kW power is sent to the grid by using high current transforms.
Exploration of protein sequence embeddings for protein-ligand binding site detection

Detection of protein-ligand binding sites is essential not only for protein function investigation but also in fields such as drug discovery or bioengineering. In this paper, we show that the recently-developed pre-trained language models can be used for protein-ligand binding site prediction. Specifically, we present a neural network architecture where inputs correspond to amino acids embeddings obtained from a protein language model. We show that increasing complexity of the language model improves the predictive performance of the method, eventually leading to results comparable to or surpassing state-of-the-art approaches. Unlike the existing methods, the presented approach does not require time-consuming computation of evolutionary information, resulting in faster running times.
Metamaterial Based Beamforming Network Integrated with Wilkinson Power Divider

This paper presents a switched beam-forming network (SBFN) integrated with a power divider for 5G applications. The proposed design consists of a metamaterial-based $4 \times4$ butler matrix (BM) and $1 \times2$ Wilkinson power (WPD) divider. The simulated results show that the proposed design achieved an outstanding insertion-loss of $-9 \pm 1$ dB and excellent reflection coefficient between the frequency range of 3.2GHz – 3.7GHz. The simulated phase differences achieved between the output ports are $-45^{\circ} \pm 8^{\circ}$ for the excitation of Input Port-1. The proposed design is suited for a 5G switched-beam antenna array (SAA) because of its enhanced bandwidth and compact structure of BM and WPD.
A Review of LOD based Techniques for Real-time Terrain Rendering

Rendering of large-scale terrain data is important in many niche applications. Advance in satellite technology and remote sensing has made it possible to scan earth's surface at higher temporal frequency and with finer spatial and spectral resolution. This generates huge amount of terrain data, in peta bytes, every second. The data is essential for analysis and as base model for societal and strategic usage. These terrain data need to be rendered for visual analysis and virtual experience in many niche applications such as flight simulator, gaming and GIS in near real time scenario. Therefore, there is a spur in research, development and optimizations of algorithms and models for indexing and rendering of large-scale digital terrain data. Level-of-Details (LOD) is one of the popular approaches for effective representation and interactive display of large terrain models. It renders a large and complex model by generating variable resolution of the same. The concepts have been applied to the polygonal model of a terrain for increasing the rendering performance and user interaction. In the past, a variety of LOD based algorithms are proposed for efficient rendering of large terrain models. This paper reviews some of the popular LOD based terrain rendering techniques over last two decades. The paper talks about various types of LOD methods and their applicability in terrain rendering. It outlines the central idea and important results of each method. Furthermore, it discusses the merits and demerits of these LOD based terrain rendering techniques.
Architecture Framework for Intelligence Orchestration in AIoT and IoT

The emergence of novel architectures such as fog and edge computing have blurred the boundaries between the network core and its periphery leading the Internet towards a computer board equivalent, while keeping the inherent distribution of the original Internet. In this paper, we present a novel architecture using an Intelligence Orchestration approach that is needed in this new network continuum computing paradigm. We aim to tackle the series of challenges of such as heterogeneity of stakeholders, use case and type of business dynamics, in addition to hardware and platforms by providing an “operating system” (OS) like design. The principles of the architecture are described and its advantages and needed next steps are also discussed.
Nutrient Requirements and Optimal Nutrition A Micronutrient Powder with Low Doses of Highly Absorbable Iron and Zinc Reduces Iron and Zinc Deficiency and Improves Weight-ForAge Z-Scores in South African Children 1 – 4

Micronutrient powders (MNP) are often added to complementary foods high in inhibitors of iron and zinc absorption. Most MNP therefore include high amounts of iron and zinc, but it is no longer recommended in malarial areas to use untargeted MNP that contain the Reference Nutrient Intake for iron in a single serving. The aim was to test the efficacy of a low-iron and -zinc (each 2.5 mg)MNP containing iron as NaFeEDTA, ascorbic acid (AA), and an exogenous phytase active at gut pH. In a double-blind controlled trial, SouthAfrican school childrenwith low iron status (n=200)were randomized to receive either theMNPor the unfortified carrier added just before consumption to a high-phytatemaize porridge5 d/wk for 23wk; primary outcomeswere iron and zinc status and a secondary outcomewas somatic growth. Comparedwith the control, theMNP increased serum ferritin (P, 0.05), body iron stores (P, 0.01) and weight-for-age Z-scores (P, 0.05) and decreased transferrin receptor (P, 0.05). The prevalence of iron deficiency fell by 30.6% (P,0.01) and the prevalenceof zinc deficiency decreased by 11.8% (P,0.05). Absorption of iron from theMNPwas estimated to be 7–8%. Inclusion of an exogenous phytase combined with NaFeEDTA and AAmay allow a substantial reduction in the iron dose from existingMNPwhile still delivering adequate iron and zinc. In addition, theMNP is likely to enhance absorption of the high native iron content of complementary foods based on cereals and/or legumes. J. Nutr. 141: 237–242, 2011.
Tilted Directional Couplers based Nolen Matrix Beamforming Network for Fifth Generation Multi-Beam Application

In this paper, a switched-beamforming network with integrated linear microstrip patch antenna array is designed at 26 GHz for fifth generation (5G) application. The 4×4 Nolen matrix formed by the tilted directional couplers and Schiffman phase shifters is proposed as the beamforming network to provide the unique progressive phase differences to the output ports for switchable multi-beams that directed at specific directions. Its performance and capability of switchable beams are assessed, analyzed and validated at 26 GHz.
A High Input Impedance Low Noise Analog Front-End Chip for Neural Signal Applications

A Capacitive Feedback Programmable Gain Instrumentation Amplifier (CFPGA) with input impedance boosting technique is proposed. Compared with traditional methods, the proposed negative capacitance programmable circuit can achieve higher input impedance without injecting current into human biopotential electrodes. Simulation results using TSMC's 180 nm CMOS technology show that the proposed input impedance boosting technique can achieve an input impedance higher than 1G. In addition, CFPGA can achieve 1–128 times of 7bit gain adjustable. The entire CFPGA achieves an input-equivalent noise density equal to $\mathbf{29}\ \mathbf{nV}/\sqrt{\mathbf{Hz}}$ at a supply voltage of 1.8V.
Automatic Control of a Soft Trunk Robot Actuated by Strings

A soft robot is a robot made of soft materials and is capable of doing complex tasks. The soft robot that was created in this experiment is a soft trunk robot actuated by strings. The purpose of this robot is to be able to move around small spaces easily and be able to perform complex tasks. For example, positioning a tip mounted sensor to gather data. From the results in the experiment, the robot can move to a target position when given a set of coordinates by using a P-controller.
Business Models, Business Strategies, and Innovations in EdTech Companies: Integration of Learning Analytics and Artificial Intelligence in Higher Education

New data-driven business models have emerged as a direct consequence of the continuous ‘datafication’ of our social reality. Correspondingly, the market for educational services is growing rapidly. There has been a dramatic shift in the education sector as a result of the proliferation of educational technology (EdTech) businesses, delivering data-driven pedagogical solutions. Despite the present state of the industry, there are still only a small number of businesses that fully use the potential of AI and LA to provide personalized educational experiences. The primary emphasis of this study is on the motivations and challenges presently influencing data-based pedagogical and curricular pathways within the context of EdTech firms. The discourse analysis shows that the conventional educational goal and the novel notion of teaching and sharing knowledge are at polar opposites. A lack of data comprehension, ambiguity, and a lack of data sovereignty hinders the creation and execution of relevant solutions, however, simultaneously the demand for adaptation and customization drives the discourse around AI-based learning systems.
A study of VHF radio wave propagation over a water surface of variable conductivity

The ground wave propagation of VHF radio waves over an inhomogeneous sea surface of variable conductivity and under various sea roughness conditions is studied both theoretically and experimentally. Theoretical simulations predict a high sensitivity of the ground wave attenuation factor upon large variations of the surface conductivity, as can be found in an estuary. This sensitivity can be influenced appreciably by the sea state under high winds in fetch-limited conditions and under a broader range of wind speeds for a fully developed sea. Results from a VHF radar experiment over a river plume with appropriate in situ documentation are reported here. They validate the theoretical expectations on the influence of the conductivity profile on radio wave propagation. Because of the low winds encountered, the wind/wave influence is evident only on the variations of the radar cross-section coefficient (linearly related to Bragg wave spectral energy) in fetch-limited conditions. Also, these variations can be attributed in some cases to the influence of slicks in the river plume. The study suggests that the VHF radar technique may be used for remote sensing of sea surface conductivity.
Performance Comparison of Machine Learning Methods in DDoS Attack Detection in Smart Grids

The integration of the cyber-network with the physical power grid makes it prone to cyber-attacks disrupting the normal operation of the grid and therefore critical to detect. This paper compares how the detection of Distributed Denial of Service (DDOS) attacks, one of the most common types of cyber-attack, on smart grids varies depending on the Machine Learning (ML) method used for detection, the different datasets used for the training, and the features of the dataset incorporated in the training. The most commonly used datasets namely KDDCup’99 and CICIDS’17 datasets are adapted for the sake of testing. The different ML methods used for these experiments are Decision Tree, Random Forest, Quadratic Discriminant Analysis, Support Vector Machine, Naïve Bayes, and Extreme Gradient Boosting. With extensive comparison analyses among the ML models based on accuracy, computation time, and storage usage, the paper demonstrates the applicability of the models in smart grids
A Hybrid Model for Depression Detection With Transformer and Bi-directional Long Short-Term Memory

Failure to diagnose and treat depression in a timely manner causes more than three hundred million people suffering from this mental health disorder worldwide. Depression, a global problem, affects not only people’s emotions, but also their physical and mental states. Early detection of depression is very important for the treatment of patients, so we need to achieve excellent accuracy and practicability of depression detection, among which the most important and challenging problem is to design an effective and robust depression detection model. To solve this problem, we propose a hybrid deep learning model, RoBERTa-BiLSTM, to extract features from depression text sequences. We know that the sequence models require a longer computation time as the processing is done sequentially. However, the Transformer models require less execution time with parallelized processing. This model consolidates the strengths of sequence model and Transformer model while suppressing the limitations of sequence model. Specifically, the model maps the words into a compact meaningful word embedding space through the Robustly optimized BERT approach, and then effectively captures the long-distance contextual semantics using the Bidirectional Long Short-Term Memory model. On the DAIC-WOZ and EATD-Corpus benchmark, our experiments demonstrate that our model outperforms state-of-art methods by a substantial margin.
Network Connectivity Analysis of Coding and Noncoding RNAs in Cancer

Cancer development is associated with thousands of coding and non-coding genes. Understanding the role of non-coding RNAs (ncRNAs) in cancer neighbourhood is of great significance for interpreting the occurrence and diagnosis of cancer. This paper first constructs a heterogeneous interaction network containing coding and non-coding genes. Then, the performance of network connectivities when applied to cancer-perturbed genes is evaluated by statistical index. We found that ncRNAs considerably improve the connectivity of perturbed genes and mediate more genes into cancer neighbourhood. Additionally, a method connected Perturbed Region (CPR) for mining cancer neighbourhood with the participation of ncRNAs in the network is proposed based on the bridging role of ncRNAs among cancer perturbed genes. Furthermore, we observed the role of ncRNAs in biologically complementing and promoting the formation of cancer-related pathways. Our results topologically promote the pattern of ncRNAs in forming cancer and biologically are of great benefit to reveal a more accurate disease relationship.
A Proposition to Effective Execution of Power-On Test Case Script over SoC under Post-Silicon-Validation

Post-Silicon-Validation (Po-Si-Val) plays a significant role in System-on-Chip (SoC) designing technique. Integrated circuits are created during manufacturing where there are many bugs that remain undetected during pre-silicon phase, here Po-Si-Val comes into picture. Various functional errors and electrical faults get detected and debugged during post-Silicon phase. As the first silicon sample arrives it is the responsibility of the Po-Si-Val team to validate the chips. This paper describes the effective execution of power-on test case script, debug techniques and debug tools that facilitate the bug-free chips to get successfully powered on. It also aims at understanding from where the problem arrives and what should be the correct approach to solve it. A power-on test case results are also attached which provides insight of the poweron debug.
Operator based vibration control for a MIMO nonlinear wing-plate system

Operator based nonlinear vibration control design for a multiple input multiple output(MIMO) wing-plate system is considered. For considering the MIMO wing-plate system with the effect of the coupling from the piezoelectric actuator(PEA)’s moments, the bounded input bounded output(BIBO) stability of the system is considered based on operator theory. Simultaneously, for obtaining the desired vibration control performance, the operators are designed to compensate for the external force and coupling’s effects. Finally, the effectiveness of the designed nonlinear control system is confirmed by the numerical simulation.
Development of a Disaster Investigation Support System Using Mixed Reality

To facilitate disaster investigation, this study proposes a disaster-reproduction system in mixed reality. Our purpose is to develop a survey-support system. The present work focuses on typhoon and torrential rain disasters. The effectiveness of our disaster-investigation support system is experimentally verified by surveying users’ experiences of the system.