Miracle-dz/UniFAR-FTUs huggingface dataset:
Wearable in-the-ear EEG system for SSVEP-based brain–computer interface

Steady-state visual evoked potential (SSVEP) has been widely used in electroencephalogram (EEG)-based brain–computer interfaces (BCIs) due to its high information transfer rate (ITR) and short training time. Current methods usually measure SSVEP from electrodes on the scalp, which is an uncomfortable and time-consuming method. Furthermore, most research relies on expensive and non-portable EEG devices. To utilise BCIs in daily life, however, these are critical issues to address. Hence, a wearable EEG device for in-ear SSVEP detection is proposed. The system is 40 × 21 × 10.5 mm3 and weighs 14.2 g, thus being light weight and wearable. Moreover, the system has a noise level of 0.11 µVrms, which is comparable with commercial EEG systems. Six subjects participated in an offline BCI experiment that consisted of six visual targets using the developed in-the-ear EEG system. The results showed a highest ITR of 11.03 ± 4.18 bits/min with an accuracy of 79.9 ± 13.1%, and the experiments demonstrated that the system can be utilised for unobtrusive monitoring of SSVEP in BCI applications.
Low-Power Multi-Lead Wearable ECG System With Sensor Data Compression

This study proposes a low-power wearable electrocardiogram (ECG) acquisition system for monitoring human health in daily life. A flexible sensing electrode was designed to replace traditional silver–silver chloride (Ag/AgCl) electrodes for measuring ECG signals from leads I, II, and V1. The traditional multi-lead-wired ECG detector limits the movements and activities of the patient and requires medical staff to assist in collecting ECG signals. The portable ECG acquisition device proposed in this article is not only comfortable to wear but also convenient for patients to use anytime, anywhere. To increase the sampling rate of the device to obtain more ECG signal information, this study proposes an innovative Huffman lossless ECG data compression algorithm based on variable-word length coding (VLC). This algorithm can efficiently realize data compression and reduce power consumption. It makes the whole system operate continuously for a long period of time and acquire abundant ECG information, which is helpful for clinical diagnosis. Experimental results show that when the sampling rate is 400 Hz, the working current is only 3.64 mA, and the compression ratio (CR) of the device can reach 2.38.
An Overview of the Quality of Life of Post Severe Brain Injury Patients within 2018-2020 Period of Time in Dr. Soetomo General Academic Hospital based on Short Form-36

Highlight: Until today, Traumatic Brain Injury is still a major cause of death, disability, and a serious health issue Traumatic Brain Injury patients have a good quality of life if they get adequate therapy and on time interventions ABSTRACT Introduction: Approximately 90 million traumatic brain injury (TBI) cases worldwide exist yearly. TBI pathophysiology varies, which may cause diverse complications. These complications may decrease the patients’ quality of life. Objective: Describing the quality of life of traumatic brain-injured patients after being treated at Dr. Soetomo General Academic Hospital Period 2018-2020. Methods: This research is a descriptive cross-sectional study using SF-36 questionnaire data from patients with post-severe brain injury at Dr. Soetomo General Academic Hospital in 2018-2020. Results: The value of the physical component (59.9) and mental component (68.6) in patients with severe brain injury at Dr. Soetomo General Academic Hospital showed a good quality of life, with values ​​in the SF-36 domains, namely physical function (58.2), physical limitations (46.7), body pain (73.6), general health (61.3), vitality (65.3), social functioning (72.5), emotional limitations (60), and mental health (76.5) is above the threshold value (50) except for physical limitations (46.7). Conclusion: Patients with severe brain injury had a good quality of life after receiving treatment in Dr. Soetomo General Academic Hospital.
Global stability and control of swirling jets and flames

Large-scale unsteady flow structures play an influential role in the dynamics of many practical flows, such as those found in gas turbine combustion chambers. This thesis is concerned primarily with large-scale unsteady structures that arise due to self-sustained hydrodynamic oscillations, also known as global hydrodynamic instability. Direct numerical simulation (DNS) of the Navier–Stokes equations in the low Mach number limit is used to obtain a steady base flow, and the most unstable direct and adjoint global modes. These are combined, using a structural sensitivity framework, to identify the region of the flow and the feedback mechanisms that are responsible for causing the global instability. Using a Lagrangian framework, the direct and adjoint global modes are also used to identify the regions of the flow where steady and unsteady control, such as a drag force or heat input, can suppress or promote the global instability. These tools are used to study a variety of reacting and non-reacting flows to build an understanding of the physical mechanisms that are responsible for global hydrodynamic instability in swirling diffusion flames. In a non-swirling lifted jet diffusion flame, two modes of global instability are found. The first mode is a high-frequency mode caused by the instability of the low-density jet shear layer in the premixing zone. The second mode is a low-frequency mode caused by an instability of the outer shear layer of the flame. Two types of swirling diffusion flames with vortex breakdown bubbles are considered. They show qualitatively similar behaviour to the lifted jet diffusion flames. The first type of flame is unstable to a low-frequency mode, with wavemaker located at the flame base. The second type of flame is unstable to a high-frequency mode, with wavemaker located at the upstream edge of the vortex breakdown bubble. Feedback from density perturbations is found to have a strong influence on the unstable modes in the reacting flows. The wavemaker of the high-frequency mode in the reacting flows is very similar to its non-reacting counterpart. The low-frequency mode, however, is only observed in the reacting flows. The presence of reaction increases the influence of changes in the base flow mixture fraction profiles on the eigenmode. This increased influence acts through the heat release term. These results emphasize the possibility that non-reacting simulations and experiments may not always capture the important instability mechanisms of reacting flows, and highlight the importance of including heat release terms in stability analyses of reacting flows.
Blind quantum machine learning

Blind quantum machine learning (BQML) enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server in such a approach that the privacy data is preserved. Here we propose the first BQML protocol that the client can classify two-dimensional vectors to different clusters, resorting to a remote small-scale photon quantum computation processor. During the protocol, the client is only required to rotate and measure the single qubit. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for quantum machine learning.
3-D Indoor Localization and Identification Through RSSI-Based Angle of Arrival Estimation With Real Wi-Fi Signals

A highly accurate 3-D indoor passive localization and identification system is presented in this article. The system can monitor various commercial Wi-Fi devices through the proposed received signal strength indicator (RSSI)-based angle of arrival (AoA) estimation technique. RSSI-based localization systems conventionally use additional assistance techniques, such as distance–RSSI calibration, fingerprint analysis, machine learning, and a widespread setup to achieve high accuracy. On the contrary, this proposed system can operate in complex environments full of scattering objects and obstructions without requiring any additional assistance techniques. The proposed technique uses a six-port network to evaluate the phase difference in the carrier waves of Wi-Fi signals without the influence of modulated signals. The network also preserves the modulated signals without the influence of the phase difference of the carrier waves such that Wi-Fi devices can be identified. Regarding practical applications, the system is designed to be capable of detecting devices through walls and thus can be hidden outside the monitored room. The experimental results indicate that in single-source localization, the average error is 0.089 m; in multiple-source localization, it is 0.354 m; and for seeing through the wall, it is 0.24 m. In the worst case scenario, the error is still smaller than 0.63 m. The accuracy in all the experimental results was found to be at the decimeter level. In summary, this study experimentally validated a 3-D indoor localization and identification system for diverse Wi-Fi devices in various environments.
Shelf-Life Prediction and Thermodynamic Properties of No Added Sugar Chocolate Spread Fortified with Multiple Micronutrients

The development of fortified healthy pleasant foods, in which saturated fats are replaced with unsaturated ones, poses a challenge for the food industry due to their susceptibility to oxidative rancidity, which decreases product shelf-life, causes the destruction of health-promoting molecules, and forms potentially toxic compounds. A comparative study applying the Arrhenius model was carried out to investigate the oxidative stability and predict the shelf-life of a newly developed no added sugar chocolate spread formulated with sunflower oil, and fortified with vitamin D, Mg, and Ca checked against two commercially available spreads: No Palm and a well-known commercially available product (RB). The results obtained from the accelerated shelf-life testing for peroxide value (PV) showed relatively higher activation energy (Ea, 14.48 kJ/mol K) for RB, whereas lower Ea (11.31–12.78 kJ/mol K) was obtained for No Palm and all the experimental spread chocolates. Q10 values were comparable (1.202–1.154), indicating a similar catalytic effect of the temperature upon the oxidation rate across all the investigated samples. The positive Gibbs free energies ranged from 75.014 to 83.550 kJ/mol and pointed out that the lipid oxidation reaction in the chocolate spread was an endergonic process. The predicted shelf-life at 293.15 K was 8.57 months (RB), 7 months (No Palm), and 6.8 months for all the experimental spreadable chocolate. However, the higher production of hydroperoxides was observed in chocolate fortified with magnesium-calcium carbonate nanoparticles and stored at 313.15 and 323.15 K, suggesting these particles may enhance lipid oxidation.
A review on the performance measurement of big data analytics process

Abstract— one vital aspect of big data is the process which handles big data, mainly referred to as big data analytics process. Studying this process and measuring it for better performance can have enormous benefits for those who are willing to harvest the value of big data. The focus on processes is more common in other disciplines such as business and software development but in big data analytic, it is relatively new. The purpose of this paper is to investigate big data analytics (BDA) process and identify suitable performance measures for it. Existing BDA processes and performance measurement methods are studied and the results are presented in this paper
Propagation model for transionospheric fluctuating paths of propagation: Simulator of the transionospheric channel

A new model for scintillation on transionospheric links, based on a hybrid method and valid for strong scintillations, has been developed and used to construct a software transionospheric channel simulator. The method is a combination of the complex phase method and the random screen technique. The parameters of the random screen are determined as the result of a rigorous solution to the problem of propagation inside the ionosphere using the extended Rytov approximation (the complex phase method). The random two‐dimensional spatial spectrum at the screen is then transferred down to the Earth's surface employing the rigorous relationships of the random screen theory. Thus the complex phase method can adequately introduce a random screen below the ionosphere for L‐band frequencies. The technique is capable of producing statistical characteristics and simulating time realizations of the field for a wide range of input parameters. Preliminary results are presented for both weak and strong scintillations.
CBIR Based on Colour Fiture Extraction with Java

Text-based image retrieval techniques that exist today can not be used to represent the image that we seek in an image database, which is often obtained by the image search results that are not desirable, because we must know the right keywords in accordance with the image file name to obtain the image we want. To overcome the problem of image retrieval, one solution is to use CBIR. Content-Based Image Retrieval (CBIR) or image retrieval is an image retrieval method by doing a comparison between the query image features with image features that exist in the database (Query by Example). CBIR method is often used similarity search based on color features, shape, and texture. As for calculating the similarity of color features using Euclidean distance. On this basis the author are encouraged to conduct research and manufacture of image search application based on color feature extraction using 2 types of histograms of the Global Color Histogram (GCHs), and Local Color Histogram (LCHs). So that it can be seen comparing the search results using the method GCHs with the search results using the method LCHs or use both types of these histograms. So get a color-based image search results more accurate. Keywords: CBIR, Global Color Histogram, Local Color Histogram, Euclidean
Structure-Guaranteed SAR Imagery via Spatially-Variant Morphology Regularization in ADMM Manner

Conventional sparsity-driven synthetic aperture radar (SAR) imagery proceeds via $\ell _{1}$ regularization, or named by least absolute shrinkage and selection operator (LASSO). However, followed by the enhanced sparse feature, structures of the scenes or targets of interest in weak scattering are easily lost. Therefore, it becomes difficult to make use of the high-resolution SAR data, and even high costs have been paid for the resolution. In this article, a novel structure-guaranteed SAR (SG-SAR) imaging algorithm is proposed by utilizing the morphology metric for the cluster feature of the scatterers of the scenes/targets of interest. By introducing structural priors in terms of morphology norm, the intended structure features can be highlighted via convex regularization. More specifically, to accommodate complicated scenarios or targets, the structure element (SE) in the morphology regularizer is designed to be spatially variant under structure tensor representation. Different from conventional convex optimizations, the proposed SG-SAR algorithm is solved under the alternating direction method of multipliers (ADMMs), which is flexible to incorporate with the super-resolution imagery. In such cases, both sparse and structural features can be simultaneously enhanced, even with limited measurements and in a low signal-to-noise ratio (SNR). Superior convergence and robustness can be guaranteed. Moreover, a grouping mask scheme is used to accommodate the complex-valued SAR data. Finally, both simulated and measured SAR data are applied for the validation. Comparisons with the conventions are performed in terms of phase transition analysis, so as to verify the superiority of the proposed algorithm both qualitatively and quantitatively.
Design and Analysis of Hybrid Fixed-Wing Type Flying Robot

With the advent of disruptive technologies, unmanned aerial vehicles have seen substantial growth over the past few years. The market for flying robots is increasing drastically, and they are getting used in various sectors. This paper is aimed at discussing the novel design of a hybrid fixed-wing type flying robot in which both fixed-wing and rotary-wing concepts are combined so that stable flight with vertical takeoff can be possible. Our design proposes a compact structure that can be efficiently used in indoor applications. We have also discussed its structural analysis, the model’s stability, the CFD analysis, and the vibrational analysis of the designed structure. The objective is to design an effective compact flying robot system that will be used for medical applications and need to carry a payload of a minimum of 2 kg with good aerodynamic performance. The aerodynamic model required for a hybrid fixed-wing type flying robot has been developed, and the static stability of the model has been evaluated in the paper.
Street Tree Extraction and Segmentation from Mobile LiDAR Point Clouds Based on Spatial Geometric Features of Object Primitives

Extracting street trees from mobile Light Detection and Ranging (LiDAR) point clouds is still encountering challenges, such as low extraction accuracy and poor robustness in complex urban environment, and difficulty in the segmentation of overlapping trees. To solve these problems, this paper proposed a street tree extraction and segmentation method based on spatial geometric features of object primitives. In this paper, mobile LiDAR point clouds were first segmented into object primitives based on the proposed graph segmentation method, which can release the computation burden effectively. According to the spatial geometric features of the segmented object primitives, stem points were extracted. In doing so, the robustness and accuracy for stem detecting can be improved. Furthermore, voxel connectivity analysis and individual tree optimization were combined successively. In doing so, the neighboring trees could be separated successfully. Four datasets located in Henan Polytechnic University, China, were used for validating the performance of the proposed method. The four mobile LiDAR point clouds contained 106, 45, 76, and 46 trees, respectively. The experimental results showed that the proposed method can achieve the performance of individual tree separation in all the four testing plots. Compared to the other three methods, the proposed method can make a good balance between the commission and omission errors and achieved the highest average F1 scores.
An assessment of generating quasi-static magnetic fields using laser-driven “capacitor” coils

Over the previous decade, numerous experiments have been performed using a laser to drive a strong, quasi-static magnetic field. Field strength and energy density measurements of these experiments have varied by many orders of magnitude, painting a confusing picture of the effectiveness of these laser-driven coils (LDCs) as tools for generating consistent fields. At the higher end of the field energy spectrum, kilotesla field measurements have been used to justify future experimental platforms, theoretical work, and inertial confinement fusion concepts. In this paper, we present the results from our own experiments designed to measure magnetic fields from LDCs as well as a review of the body of experiments that have been undertaken in this field. We demonstrate how problems with prior diagnostic analyses have led to overestimations of the magnetic fields generated from LDCs.
A Proposed Framework of Market-Oriented Ethnography (MOE) Approach to Study Co-Creation of Content Through Product Storytelling

Abstract The article aims to understand the product storytelling process while co-creating the content with suppliers and consumers for an end-to-end brand experience by integrating stages of storytelling with S-D logic actors and activities, using the DART value creation model. Marketing-oriented ethnography was conducted for thirty days for the research needs of this study. We collected findings of in-depth interviews with various stakeholders of two organizations. The data analysis resulted in a unified framework of different activities performed by suppliers, organizations, and consumers at various product storytelling stages. We find that suppliers focused on sensory, affected, and relational dimensions; the organization emphasized cognitive, relational, and behavioral dimensions, and consumers aimed at sensory, behavioral, cognitive, relational, and affective dimensions. The framework states that different brand experience dimensions can be understood by multiple stakeholders while co-creating the content.
Early Postnatal Care: Exploring Primary Caregivers’ Experiences of Continuity of Care After Discharge at Selected Districts in Limpopo Province South Africa

Background Early postnatal discharge is perceived as a factor that contributes to possible maternal, neonatal complications and deaths during the first week of delivery. Continuing with provision of home-based postnatal care by the primary caregivers is crucial to prevent morbidity and mortality. Purpose The purpose was to explore the experiences of primary caregivers of continuing with the provision of early postnatal care at home. Method A qualitative research approach, which was explorative, descriptive, and contextual was conducted in the three selected districts of different ethnic groups at Limpopo Province in October 2019. A non-probability, purposive sampling was used to conveniently sample 20 primary caregivers of postnatal women. Data were collected during the first 2 weeks of delivery through in-depth individual semi-structured interviews until data saturation was reached. Data were analysed through a thematic analysis framework applying Tesch’s open coding method. The study was done in Limpopo district, South Africa. Findings The findings revealed a theme, namely, knowledge and skills of providing community-based postnatal care and three sub-themes; early postnatal care to detect and prevent possible complications to the mother; general postnatal care, and early neonatal care to detect and prevent possible complications. Conclusion Primary caregivers displayed acceptable indigenous skills and knowledge of providing early postnatal care. However, the skills of detecting potential danger signs were not adequately displayed. It was recommended that midwives need to create awareness to primary caregivers from the antenatal through postnatal care period. Community Health Workers to assist during immediate postnatal check-ups (mother and neonate) which is done within 72hours of delivery. The health education, counseling, and community-based awareness campaigns are to be done with a focus on the detection and prevention of possible complications to mother and baby.