Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT Health Monitoring Device of Oxygen Saturation (SpO2) and Heart Rate Level

People are normally suffering from body sickness such as heart disease, high blood pressure and diabetes when getting older. Thus, the health of elderly should be monitored to prepare for any emergency cases. This research presents a realtime monitoring system for elderly that is able to measure heart rate and Peripheral Capillary Oxygen Saturation Level (SpO2). The monitoring system is constructed using MAX30100 as front-end sensor and Node MCU (ESP8266) is used as microcontroller to collect and transfer the data to Cloud. Five healthy subjects have been chosen properly and their SpO2 and heart rate level are collected. All data undergone a few processes for validation such as segmentation and filtering. For SpO2, the data are computed to IR/RED variables. Then, the IR/RED are processed to get SpO2 ratio using empirically derived calibration curves in order to produce normal and abnormal results. For heart rate, a correlation test is conducted between the experimental reading with the reference reading. For the monitoring system, both SpO2 and heart rate data are combined to obtain the final classification of normal and abnormal. The result of the correlation test shows strong correlation value (rs=0.993). The percentage error is calculated between the developed system with a commercial oximeter which is resulted with less 3% and 1.03 % for SpO2 and heart rate, respectively. Based on the validation results, the monitoring system of SpO2 and heart rate is ready to be used. Also, the IoT system allows many authenticated users to monitor the patient condition.
A Multi-Frequency-Point Algorithm for Frequency-Selective IQ imbalance Estimation in Massive MIMO System

Massive MIMO is a key technology of future wireless communication systems to obtain high data rate and high spectrum efficiency. However, our effort was destroyed by hardware nonlinearity including IQ Imbalance especially. When spectrum in usage is wide, frequency dependent IQ imbalance is more serious and it is difficult to combat with the performance degradation caused by that. In this paper, we proposed a method based on interpolation of sub-band results to mitigate the effects of frequency dependent IQ imbalance, which is independent with modulation method of data symbols and has higher channel rate. The theory analysis and simulation results both show that our algorithm can decrease the estimation MSE, and improve the overall performance including data rates and spectrum efficiency proofed by simulation.
Wind Speed Prediction in the area of PLTB Tolo Jeneponto South Sulawesi using Artificial Neural Network

Forecasting the output power of a wind turbine is very much determined by the ability to predict wind speed at the location of the wind turbine placement. The results of this forecast are highly correlated with the operating patterns that will be applied to the electric power system and also with the system operating costs. Wind speed forecasting at PLTB Tolo Jeneponto, South Sulawesi, Indonesia is done by taking wind speed data for the last 20 years. The method used in forecasting is an Artificial Neural Network. From the simulation results, it can be seen that the forecast error is 0.17883 percent. This shows that the ANN method can be accepted as a method in predicting wind speed.
Assessment Methods and Interventions to Develop Computational Thinking — A Literature Review

Computational Thinking (CT) allows us to solve complex problems by expressing it in a way that computers, humans, or both, can understand. CT is closely related to problem solving and critical thinking, which are actively used in STEM education. Research on CT has gained steady momentum recently, and many of the institutions around the world have adopted CT into their curriculum. This paper presents a literature review conducted to identify the interventions used to develop CT skills and the methods used to assess CT skills. The review explored various publications on CT and identified that curriculum-based interventions and workshops are the primary interventions used for introducing CT in the educational sector. During the analysis, we have also identified a handful of assessment tools used for measuring CT skills, but they are not standardized assessment tools. Hence, we plan to analyze the existing assessment methods and propose a new sophisticated tool for evaluating CT skills as our future work.
Bi-Tier Differential Privacy for Precise Auction-Based People-Centric IoT Service

With the fast proliferation of device sensing and computing, crowed sensing has become the building block of the Internet of things. Consequently, various data collection and incentive mechanisms are investigated for people-centric services. In this paper, we have investigated the problem of privacy-aware people-centric IoT service based on a tailored auction approach. We applied a bi-tier differential privacy methodology on the data collected from crowdsensing IoT devices. A corresponding pricing scheme is also proposed to ensure the property of incentive compatibility, precise service data, and anonymized query results. Comparing to traditional privacy-aware auction schemes which only focus on the cost, our corresponding precise privacy-aware auction scheme provides a tailored IoT service based on the customers’ request. The proposed trial query technique is able to provide a precise assessment of service quality, thus improves the efficiency of the people-centric IoT service. The customer could enjoy the convenience of service evaluation before making a bid, while the actual service data is anonymized to guarantee the service providers’ interests. We evaluate the proposed bi-tier differential privacy schema for auction-based service by conducting extensive simulations. The experimental results show that our proposed method yields higher data utility and accuracy for the IoT service customers with privacy concerns.
Data mining and Region Prediction Based on Crime Using Random Forest

Crime is one of the most critical problems of the today’s world which affects the normal life of the society and it breaks the social, and economical flow of a country. The scenario is the same for Bangladesh. This paper focused on prediction of the areas based on crimes instead of the opposite. This will also be beneficial to predict crimes occurred in different cities in certain timeframe. And knowing the future trend the law enforcement and detectives will be able to take precautionary steps which will in turn reduce the rate of such events. Here, Random Forest algorithm is used to predict the regions (AKA district) based on different crime rates.
Internet of Trees (IoTr): A Low-Cost Single Stub Lorentz Resonator For Plant Moisture Sensing

The water content of plants plays a significant role in the safety and maintenance of plants' health. Low moisture content in leaves can damage the plants, thus early detection of water content requires very high sensitivity sensors. In this paper, we propose a plant moisture sensor based on Lorentz dispersion to detect plant's moisture level of different concentrations. The high sensitivity of Lorentz sensor comes from its narrowband resonant characteristics manifested by rapid slope change of its phase spectrum, a phenomenon known as anomalous dispersion. This unique signature in its phase spectrum adds a second degree of freedom in sensing and detection, as well as removes ambiguity in the identification of the resonant frequency. We practically measured the moisture content of different samples of leaves and analyzed quick distinctive deviations in the phase and magnitude responses of the sensor based upon the water content in the leaves. The proposed plant moisture sensor is supposed to be integrated with an internet network to inform the farmers and agriculturalists about plants' health. In this way, the concept of Internet of Trees (IoTr) can be employed. Consequently, timely watering of the plants, as well as appropriate usage of water will be guaranteed.
Frequency Control for Stand-Alone Hydro Power Plants using Ant Colony Optimization

Future generation will mostly depend on renewable energy resources due to lack of conventional energy resources, hence existing renewable energy resources are being modified for efficiency improvement and enhancement of power generation. Water is a huge source of renewable energy used in hydro power plants. Modelling of hydro power plant is shown in this paper and also a complete simulation model is designed. The purpose of this paper is to control the output frequency of hydro power plant. Variation in flow of water disturbs the speed of hydraulic turbine which results in variable voltage and frequency at the generator output. Therefore in order to obtain the constant frequency at the hydro power plant a PID controller is being used in governor. In this research, the controller parameters are obtained using two techniques ZeiglerNichols (Z-N) technique and Ant Colony Optimization (ACO). ACO is used to overcome the problems in traditional Z-N technique. The frequency output of the hydro power plant using both techniques are compared which proves that ACO is better than Z-N technique. This paper work can be used for practical purposes and theoretical analysis.
A Multi-Contact-Aided Continuum Manipulator With Anisotropic Shapes

Cable-driven continuum manipulators have shown excellent benefits to work for endoluminal intervention. Demand of small diameter for confined anatomy limits the usage of multiple actuations, leading to limited DOFs and bending shapes. To address this problem, this letter proposes a multi-contact-aided continuum manipulator with anisotropic bending shapes. First, contact-aided compliant mechanisms (CCMs) are configured at different locations to introduce the specific constraints to enable the anisotropy. Then, the forward and inverse kinematic models considering contact blocks are built. The reachable workspace of the continuum manipulator is given, and simulation cases are studied to demonstrate the superiority of the proposed manipulator. Finally, a 3D-printed physical prototype is fabricated, and preliminary experiments are conducted to evaluate the model accuracy. Results show an average shape error of 1.98 mm (4.2%) and a maximum of 3.58 mm (7.6%). A robotic bronchoscopy platform integrated with the continuum manipulator is developed to conduct the airway phantom experiments. The shape deviation of the proposed manipulator from the centerline of the bronchus is 31.8% smaller than that of the constant curvature manipulator. The experiments validate the system-level feasibility and effectiveness of the developed continuum manipulator.
Identification of synchronous generator parameters from operating data during the short-circuit from no-load operation

This paper presents the method for the identification of synchronous generator parameters during the short-circuit from no-load operation mode. The armature phase current is measured and afterward used to extract some of the unknown parameters. The mathematical derivation of the armature current during the short circuit from the no-load operation mode is derived from the well-known mathematical model of the synchronous generator which is based on Park’s transformations. Precisely, the mathematical formulation is expressed with the operational transfer functions in the Laplace domain, which consist of the unknown generator parameters. The determination of the parameters, based on the measured operational data, is carried out using popular computational intelligence, or metaheuristic algorithm.
PRECOSE - An Approach for Preserving Color Semantics in the Conversion of Colors to Grayscale in the Context of Medical Scoring Boards

Medical visualization often contain colors that highlight important information. When printing medical scoring boards in grayscale, the semantic information of colors is lost. Hence, visualizations relies on text-based information for the reader to comprehend images in grayscale. Common grayscale mapping methods do not preserve the special semantics of colors. In medical scoring boards, colors that are used are red for critical, yellow for warning, green for harmless, and blue for without any concerns. To overcome problem of information loss, we present PRECOSE, a method for the color to grayscale conversion for medical scoring boards, which enables to PREserve COlor SEmantics in a grayscale color space. With PRECOSE medical scoring boards do not longer have the need for textual features. They can be printed in grayscale without the loss of colors' semantics.
A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-to-end behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.
Brain-Controlled Wheelchair Review: From Wet Electrode to Dry Electrode, From Single Modal to Hybrid Modal, From Synchronous to Asynchronous

Brain-computer interface (BCI) is a novel human-computer interaction model, which does not depend on the conventional output pathway (peripheral nerve and muscle tissue). In the past three decades, it has attracted the interest of researchers and gradually become a research hotspot. As a typical BCI application, the brain-controlled wheelchair (BCW) could provide a new communicating channel with the external environment for physically disabled people. However, the main challenge of BCW is how to decode multi-degree of freedom control instruction from electroencephalogram (EEG) as soon as possible. The research progress of BCW has been developed rapidly over the past fifteen years. In this review, we investigate the BCW from multiple perspectives, include the type of signal acquisition, the pattern of commands for the control system and the working mechanism of the control system. Furthermore, we summarize the development trend of BCW based on the previous investigation, and it is mainly manifested in three aspects: from a wet electrode to dry electrode, from single-mode to multi-mode, and from synchronous control to asynchronous control. With the continuous development of BCW, we also find new functions have been introduced into BCW to increase its stability and robustness. It is believed that BCW will be able to enter the real-life from the laboratory and will be widely used in rehabilitation medicine in the future.
A fundamental wax deposition model for water‐in‐oil dispersed flows in subsea pipelines

Water-in-oil dispersions frequently form in subsea oil pipeline transportation and their presence affects the wax deposition rate in subsea pipelines. A fundamental model for wax deposition on the wall of water-in-oil dispersed phase flow pipelines has not been developed. Dispersed water droplets can affect the heat and mass transfer characteristics of wax deposition and alter the deposit growth rate. In this study, wax deposition from water-in-oil dispersed flows is comprehensively modeled using first principles of heat and mass transfer. The role of the dispersed water phase on the heat and mass transfer aspects of wax deposition is analyzed. The developed model predicts different effects of the water volume fraction and droplet size on the wax deposition rates in laboratory flow loop experiments and in field scale wax deposition processes. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4201–4213, 2017
Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep Learning

Water leakages are very important signals that characterize the serious potential structural damages or flaws in shield tunnels. Automatic, timely, and accurate detection of water leakages is of great significance to the safe operation and maintenance for shield tunnels. However, existing methods (e.g., passive optical images) are highly limited by the confined spaces and dim light conditions in shield tunnels. This paper proposes a new method that uses the intensity images of terrestrial mobile LiDAR (Light Detection and Ranging) for automatic leakage detection in shield tunnels based on deep learning. A self-developed terrestrial mobile LiDAR system (Faro Focus X330) are used to simultaneously obtain the point clouds and intensity information of the shield tunnels. The original intensity data are corrected for the distance effect to generate intensity images. A improved Fully Convolutional Network (FCN) based VGG-19 that is a network structure in deep learning is constructed to achieve accurate tunnel leakage detection based on intensity images. The results show that the proposed method can rapidly and accurately detect leakages in shield tunnels and the leakage detection results are not affected by the tunnel attachments (e.g., bolt holes, cables, and metal facilities). The error rate and segmentation speed are 4.8% and 0.6 s, respectively.
Robust Pedestrian Dead Reckoning for Multiple Poses in Smartphones

In this paper, an integration approach (IA) and parametric approach (PA) fusion-based pedestrian dead reckoning (PDR) using low-cost inertial sensors is proposed to improve the indoor position estimation. When moving indoors using a smartphone, various poses such as texting and putting a pocket are possible. In the existing PA-based positioning algorithms, it can be used only when the pedestrian’s walking direction and the device’s direction coincide. In order to solve the problem, this paper proposes an algorithm that constructs state variables based on the IA and uses the position vector from the PA as a measurement. If the walking direction and the device heading do not match based on the poses, the position is updated according to the direction calculated using principal component analysis (PCA) and the step length obtained from the PA. Through experiments considering various operating conditions and paths, it shows that the proposed method stably estimates the position and improves performance even in various indoor environments with the accuracy of 2.13m for Xsens MTw 2.49m for Smartphone.
A Small-Scale Wireless Distributed Cooperative Secure Communication Network Design Using Graph FIR Filters

This letter presents a small-scale wireless distributed cooperative secure-communication network (WDCSN) design using a novel spectral graph FIR filter to achieve desired secrecy capacity at the intended receiver in the presence of single and multiple eavesdroppers. An undirected weighted graph <inline-formula><tex-math notation="LaTeX">$\mathcal {G} (V, \mathcal {E}, W)$</tex-math></inline-formula>, with relays, eavesdroppers, source, and destination are as nodes (<inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula>), and circularly symmetric complex Gaussian random variables as edge weights (<inline-formula><tex-math notation="LaTeX">$W$</tex-math></inline-formula>) are considered to represent the WDCSN. A graph Laplacian is used as a graph shift operator to design the proposed filter. Filter coefficients are calculated by using the least squared error as a criterion. Simulations are conducted for a two-way WDCSN to achieve the desired secrecy capacity using different graph structures considered for WDCSN. The proposed filter design's performance is quantified by using the secrecy outage probability metric. The results show that desired secrecy capacity with SOP of <inline-formula><tex-math notation="LaTeX">$10^{-2}$</tex-math></inline-formula> is achievable using the proposed methodology, irrespective of graph structures with variable complexity. An SOP performance gain of <inline-formula><tex-math notation="LaTeX">$(60\! -\! 80) \%$</tex-math></inline-formula> is achieved over the SOP reported in the literature.
Effect of benzophenone-3 at the environmentally relevant concentration on the liver of Zebra fish (Danio rerio (Hamilton)

The widespread use of Benzophenone-3 (BP-3) has resulted in its release into the ecosystem, and its potential impact on the aquatic ecosystem is of rising concern. Therefore, the current investigation was conducted to analyze the effect of BP-3 at environmentally relevant concentration (44 μg/L in river) in the zebra fish liver through the biochemical markers (protein, glycogen and lipids), hepatic markers, oxidative stress markers and histopathological analysis. The zebra fish was exposure to BP-3 at environmentally relevant concentration for 45 days. During different interval viz. 15, 30 and 45 days, all the above-mentioned markers in the liver and it’s histology were analyzed. The biochemical parameters like glycogen, protein triglyceride content were significantly declined with increased total cholesterol level in the zebra fish liver. The activity of hepatic markers like Alanine aminotransferase (ALT) and Aspartate aminotransferase (AST), lipid peroxidation markers viz. thiobarbituric acid reactive substances (TBARS) and Hydrogen peroxide (H2O2) were found to be significantly higher meanwhile the activity of antioxidant superoxide dismutase (SOD), catalase (CAT), Glutathione peroxidase (GPx) and Glutathione (GSH) activity were found to be as a significant reduction in the liver of BP-3 treated fish for 30 and 45 days. Furthermore, the liver histology also showed abnormal changes in their morphology when compared to control. At the duration of 15 days, BP-3 exposure slightly altered all the parameters, but the values were non-significant when compared to control fish. From these results, we conclude that the treatment of BP-3 at environmentally relevant concentration can alter the physiology of the zebra fish liver.