Miracle-dz/UniFAR-FTUs huggingface dataset:
Resting state EEG signal analysis in Indian Dyslexic children

Developmental Dyslexia is the most common learning disability associated with the difficulty in reading skills even with normal intelligence. The present study analyzed the resting state (eyes closed) 19 channel EEG recordings in 15 Indian dyslexic children and 15 normal children. Node strength for each electrode location for all major EEG bands (delta, theta, alpha, and beta) was calculated for both groups and compared. Results showed increased (p<0.05) delta/theta node strength in temporal, parietal, frontal and occipital regions during rest in the dyslexic group. Also, the dyslexic group showed decreased (p<0.05) alpha/beta node strength at right temporal, prefrontal and left central brain areas. The control group showed decreased (p<0.05) delta/theta node strength at temporal, parietal, occipital and frontal regions and increased (p<0.05) alpha/beta node strength at the right parietal and frontal regions. The present study concludes with the finding of increased theta activity during rest condition (eyes closed) as a strong biomarker for the existence of resting-state functional abnormalities in dyslexic children at temporal, parietal and occipital regions.
Parameter Estimation and Self-Commissioning of High Power Induction Motor Drive: Importance with a Case Study

In this paper, a simple approach to estimate the parameters of induction motor drive is presented along with the importance of parameter estimation and self-commissioning of an induction motor drive. In the proposed method, parameters such as stator resistance, stator transient inductance and rotor time constant are estimated and used for designing and tuning of PI controllers for a vector controlled induction motor drive, which is the prerequisite for self-commissioning of drive. The parameters estimated through this method takes care of variation in resistance and inductance based on site condition, compared to design parameters of the machine. The proposed parameter estimation method is validated on 875 kW induction motor and found in-line with the design values of the machine and the field conditions. The proposed method focuses on high power machines in standstill condition because of limitations in conducting NO load and blocked rotor tests to obtain the machine parameters. However, the same method is also validated on 5 HP induction machine and found in-line with the test parameters. The paper also presents a case study of forced draft fan application for a boiler in a refinery plant, where one of the drive is commissioned with the design values of the machine instead of self-commissioning procedure resulting in malfunctioning of drive operation for particular speed range, which lead to repeated failure of motor fan coupling, sheering of coupling bolts and damage in drive end bearings.
Classifying DNS Servers Based on Response Message Matrix Using Machine Learning

Improperly configured Domain Name System (DNS) servers are sometimes used as packet reflectors as part of a DoS or DDoS attack. Detecting packets created as a result of this activity is logically possible by monitoring the DNS request and response traffic. Any response that does not have a corresponding request can be considered a reflected message; checking and tracking every DNS packet, however, is a non-trivial operation. In this paper, we propose a detection mechanism for DNS servers used as reflectors by using a DNS server feature matrix built from a small number of packets and a machine learning algorithm. The F1 score of bad DNS server detection was over 0.9 when the test and training data are generated within the same day.
Learning How Spectator Reactions Affect Popularity on Twitch

Gameplay live streaming has become a highly demanded online entertainment traffic on services like Twitch.tv. The live chat concurrent with on-air streams allows viewers to talk about the content with each other. This new form of online content carries numerous spectator reactions, including text and emojis, and brings a unique opportunity to predict which video becomes popular in the end. This study presents a prediction model of viewer counts on a newly compiled dataset describing the video popularity of Twitch live streams and chat reactions of spectators. Our analysis demonstrates that the spectator reactions captured from the early-stage of live streams, such as the first 15 minutes out of several hours of content, hold essential markers of the eventual popularity of video streams. We discuss the implications of the findings and future directions.
Multiple-Input Single-Output Universal Biquad Filters Using Reduced Number of OTAs

This paper presents two three-input and singleoutput electronically controllable voltage-mode universal biquad filter configurations employing four single output operational transconductance amplifiers (OTAs) and two grounded capacitors (ideal for integrated circuit implementation and absorption of shunt parasitic capacitances). The presented structures provide second-order low pass filter (LPF), high pass filter (HPF), band pass filter (BPF), band reject filter (BRF) and all pass filter (APF) responses using appropriate selection(s) of three inputs. The cut-off frequency (ωo ) and quality factor (Qo) can be tuned orthogonally through the transconductance of the OTAs. The proposed biquad filter configurations enjoy low active and passive sensitivities. The workability of multifunctional biquad filter topologies has been confirmed through PSPICE and MATLAB simulations.
Investigation of Resonant Transition Converter Performance for Step-up/Step-down Operation

The paper presents the steady-state analysis of the step-up/step-down operation of the full bridge resonant transition converter (FBRTC). In spite of several work being reported in the literature on the FBRTC converter, the performance differences during the step-up and step-down operations are less investigated. Influence of leakage inductance, magnetizing current of the transformer and switching frequency on the steady-state DC-voltage gain of the step-up/step-down converters is presented with the supporting results. The iterative design methodology for the step-up/step-down converters are proposed to enhance the efficiency. The investigation carried out in this work help the designer to select the appropriate leakage inductance, magnetizing inductance, turns ratio of the transformer and switching frequency of the step-up and step-down converters. The analysis and design methodology are validated in simulations. The performance analysis is verified in a 500 W experimental prototype.
Wavelet-ANN Based Fault Location Scheme for Bipolar CSC-Based HVDC Transmission System

Wavelet transform (WT) is a widely used method for analyzing fault transients in the faulty signals to achieve fault diagnosis. It enhances the efficiency and accuracy in the operation of Artificial Neural Network (ANN) when used as a companion. The proposed work introduced an accurate way to predict the fault location for bipolar Current Source Converter (CSC) based High Voltage DC transmission system using ANN with the combination of WT. The proposed algorithm uses the extracted features of AC sinusoidal voltage, DC voltage and current signals achieved at both poles of the rectifier end in the line. The input signals are sampled at 1 kHz which is less in comparison with the other existing techniques. The numerous simulation results validate the efficiency and stability of the proposed ANN-based strategy to deviation in the fault resistance and different fault locations. The proposed strategy estimates the location of fault with mean percentage error of 0.0289%. The advantage of this strategy is that it does not necessitate any telecommunication system as it utilizes the measurements of only rectifier end.
Detection and Location Estimation of Object in Unmanned Aerial Vehicle using Single Camera and GPS

It is important for an Unmanned Aerial Vehicle (UAV) to detect any object in its view. This enables the UAV to locate the object with respect to itself and is required for locking and tracking the object. Object detection and location estimation makes the UAV capable of manipulating the environment as well as to follow the target. In the present work, object detection has been carried out using You Only Look Once (YOLO) to detect the object in the image stream of the Robot Operating System (ROS) bag file. The GPS information of the UAV is used to further calculate the GPS coordinates of the object. The images are acquired using a single monocular camera.
Sex Comparisons for Very Short-Term Dynamic Constant External Resistance Training

This study compared sex responses for strength and barbell velocity from very short-term resistance training (VST, consisting of 2–3 training sessions) for an upper body dynamic constant external resistance (DCER) exercise (bench press [BP]). Ten females (mean ± standard deviation (SD) age: 21.3 ± 3 years, height: 166.2 ± 6 cm, body mass: 71.4 ± 10.7 kg) and 10 males (mean ± SD age: 24.6 ± 4 years, height: 179.5 ± 8 cm, body mass: 88.6 ± 11 kg) completed a pre-test visit to determine the BP 1 repetition maximum (1RM) as well as the mean (BPMV) and peak (BPPV) barbell velocities from the BP 1RM. The VST involved three training visits where the participants performed 5 sets of 6 repetitions, at 65% of the 1RM. The post-test followed the same procedures as the pre-test visit. There were significant increases in 1RM strength for both the males (5.1%) and females (5.4%) between pre-test and post-test. There were no significance differences between sex for mean (BPMV) and peak (BPPV); however, overall there was a 32.7% increase in BPMV and a 29.8% increase in BPPV. These findings indicated an increase in strength and barbell velocity for both males and females as a result of VST upper body DCER exercise in untrained subjects.
Prevalence and Molecular Characterization of Scrub Typhus in Sub-Urban Regions of Vijayawada, Andhra Pradesh, India

Rickettsial diseases are considered as some of the most covert emerging and re-emerging diseases and are being increasingly recognized. Among these, scrub typhus is the commonest occurring rickettsial infection in India. Scrub typhus is widespread in the AsiaPacific region, known as the “tsutsugamushi triangle” (Chang, 1995). It is prevalent in many parts of India and outbreaks are reported during the cooler months of the year, usually June to November (Dumler and Siberry, 2007 and Mathai et al., 2003). According to the World Health Organization, “Scrub typhus is probably one of the most under-diagnosed and under-reported febrile illnesses requiring hospitalisation in some regions” (WHO, 2006). Due to lack of awareness among clinicians the condition is labelled as “fever of unknown origin” (FUO) (Mahajan, 2005 and Walsh et al., 2001).
Predictive Control of Three Level Bidirectional Converter in Bipolar DC Microgrid for EV Charging Stations

This paper proposes model predictive control (MPC) of a bipolar bidirectional buck/boost converter derived from three level converter (TLC) configuration for integrating with electric vehicle charging station or battery energy storage system (BES) in bipolar dc microgrid structure. Bipolar dc microgrid considered here consists of two solar PV systems, dc loads and battery. The bidirectional power flow between grid and battery or EV charging stations is controlled considering the battery state of charge (SOC), total power generated and load demanded. Advantage of this converter is that it can address the dc grid voltage regulation and capacitance voltage balancing issues during variation of load and solar irradiation in bipolar dc microgrid. State space analysis is done and discrete model is developed. Simulation is done in Simulink MATLAB and analysed for voltage unbalance issues of bipolar dc microgrid under varying conditions of photovoltaic generations and load disturbance. Real time performance is tested and verified in hardware in loop environment using Typhoon HIL 402.
Method of Predictive Maintenance for Induction Furnace Based on Neural Network

Induction melting furnace is the heating chamber induct electricity to melt metal. The Induction melting furnace is widely used for modern foundries, which manufacture their products by casting or ironmaking. In the operating process, heat from melted metal makes a crack on the furnace wall. After the period, maintenance must be proceeded to prevent an explosion caused by the cracked wall. In this paper, we proposed a method to predict the maintenance point of Induction melting furnace based on the neural network. The methodology is configured by three-steps. The first step is detecting the manufacturing cycle from furnace data. The second step is capturing the value from each cycle. The third step is to predict the maintenance point by using a neural network model. By this method, we are able to predict the maintenance point with high accuracy
A Literature Review on Application of Machine Learning Techniques in Pancreas Segmentation

Pancreas segmentation provide radiologically and clinically significant information for diagnosing diseases related to pancreas. Moreover, it also helps to plan pancreatic surgery. Pancreas segmentation is a challenging task due to very high shape and volume variability of the pancreas among different patients. In this paper, we have reviewed machine learning and deep learning-based approaches applied by various researchers for pancreas segmentation. These algorithms have a mean Dice Similarity Coefficient (DSC) of 78.35% with maximum and minimum DSC as 89.7% and 56.46% respectively. The availability of better computation power and large datasets will further enhance the performance of pancreas segmentation and related tasks. The approaches presented in this paper can be easily modified to the multi organ segmentation methods by changing the loss function. The study revealed the challenge of quantizing network while maintaining performance accuracy.
A Dual VSG-Based M3C Control Scheme for Frequency Regulation Support of a Remote AC Grid Via Low-Frequency AC Transmission System

This paper proposes a low-frequency ac (LFAC) transmission system capable of providing frequency regulation support for a remote ac grid aiming at enhancing its frequency stability. The LFAC transmission system using modular multilevel matrix converters (M3Cs) for a direct ac-ac conversion is receiving noticeable attention as an alternative solution for a long-distance transmission system. One of its potential applications is the system connection between a large ac grid and a remote ac grid, whereas the latter usually suffers from a fluctuant frequency due to lack of inertia. The core of this work is addressing a novel control scheme of the remote ac grid-side M3C, which makes both interactions with the LFAC system and the remote ac grid behave like a synchronous generator using a dual virtual synchronous generator (VSG) control scheme. This control scheme can enhance the frequency regulation in the remote ac grid, which the existing control schemes cannot provide. Average dc capacitor voltage versus active power ( $V_{ave}$ -P) droop control is proposed to coordinate the power flow between the VSG controls applied in both sides of the M3C. To demonstrate the problem of the existing control scheme, as well as the benefits offered by the proposed control scheme, transient performance including load variation and fault events in the remote ac grid is studied and examined in the EMTDC/PSCAD software environment.
Decentralized Energy Scheduling of LV Microgrid Under Stochastic Environment

The integration of multiple sources and loads in the grid, necessitates the need for an efficient energy management system. The energy management system can optimally coordinate different energy sources and loads subjected to different uncertainties to achieve demand-supply balance. In this paper, optimal scheduling of local energy sources is done with the utilization of the summed-up capacity of plug-in electric vehicle technology to minimize the operating cost of the microgrid. Due to intermittent and variability of RES sources and randomness in load, uncertainty is handled by the stochastic process with scenario generation and reduction method. The efficacy of the proposed approach is investigated on the residential feeder of CIGRE LV microgrid benchmark model. To test the feasibility of study, technical and economic constraints are considered while implementing a mathematical optimization model. Four cases of microgrid operation are considered for testing, namely islanded mode, grid-connected with the trade of energy in import power mode only, export power mode only and lastly both import-export power mode. The mathematical model is prepared and solved by YALMIP integrated into the MATLAB environment, and CPLEX solver is used to reduce computational time.
Comprehensive Investigation on IoT based Smart HealthCare System

Internet of Thing (IoT) is seen as a promising technology in the healthcare system for continuously monitoring of patient health. The IoT based Smart healthcare System (SHCS) is a collaborative interconnection of healthcare devices, embedded system and internet through the smart devices. This connected device provides various application and services for vital signs monitoring, along with detection and diagnosis ofvarious diseases. A comparative study on different IoT based healthcare system, services and their key component have been presented in this paper. The exhaustive investigations on the classification of the IoT based SHCS have been carried out. In addition, recent trends and advancement of the SHCS system have been discussed along with issues and challenges in the context of futuristic IoT based healthcare system.
End-to-End Network Slicing for Flash Crowds

End-to-end network slicing is a novel concept based on virtualization and softwarization technology that can efficiently address the problems of legacy networks. It can leverage agile physical and network layer adaptability, and foster optimal user and system performance. These features make end-to-end network slicing suitable for scenarios such as flash crowds and emergency situations. This article presents an end-to-end network slicing framework capable of addressing the demands of flash crowd and emergency scenarios. The article provides details about these scenarios, and it introduces an end-to-end agile, flexible, and scalable wireless system architecture consisting of softwarized components that can be orchestrated to fulfill the underlying system requirements. By addressing important key performance indicators with experimental measurements, the article demonstrates the applicability of our slicing framework for flash crowd scenarios.
Comparative analysis of Lung sound denoising technique

Pulmonary disorders are causing a huge increments in the mortality rate. The main reason behind this is misinterpretation or delay in diagnosis of disease. Developing a computerized system which can help clinicians to remove this problem will give a better result in controlling these adverse effects of increasing mortality rate. Data collection and their preprocessing with the help of computerized system will tend to remove the chances of overlapping of results which may cause misinterpretation. For developing a computerized system. Data from 15 patients have been collected in the time period of one month. Along with the results of pulmonary function test the overlapping are easily visible which can be overcome by including heart sound and lung sound in our analysis. However, lung sound signals are subjected to different kind of noises. Therefore, six denoising techniques are Wavelet, Savitzky Golay Moving average filter, FIR, Median filter and Butterworth filter are implemented and evaluated. The performance of all the three filters are compared on the basis of signal to noise ratio. Wavelet denoising technique gives better result with a Signal to noise (SNR) value of 84.43dB.
Effects of Physiologic, Metabolic and Molecular Adaptations to Calorie Restriction on Biomarkers of Longevity

Calorie restriction (CR), reducing caloric intake without malnutrition, increases lifespan and delays the onset of age-related diseases. Characterizing the underlying mechanisms that mediate the effects of calorie restriction on aging and lifespan will provide insight into the fundamental biology of aging, as well as guide research into the development of therapeutics for age-related diseases. It seems likely that some combination of physiologic, metabolic and molecular adaptations to CR lead to cellular responses that in-turn increase the longevity of the organism. Thus the goal of this thesis work was to combine a kinetic biomarker strategy with classic physiologic and molecular techniques to determine the role of physiologic adaptations, fat metabolism and molecular signaling on biomarkers of CR-induced longevity in mice. The data presented here demonstrate that CR leads to significant reductions in cell proliferation rates in keratinocytes, liver cells, mammary epithelial cells and splenic T-cells. These reductions in cell proliferation rates cannot be accounted for by reductions in food intake, energy expenditure, fat mass or body weight. In addition, the CR-induced reduction in cell proliferation is not dependent on Sirt1 expression, nor can it be mimicked by resveratrol treatment. However, reductions in cell proliferation rates were associated with a CR-induced increase in whole body fatty acid oxidation and have a strong negative correlation with circulating IGF-1 levels. Taken together these results suggest that increased reliance on fatty acid oxidation and reductions in IGF-1 signaling may be metabolic pathways that mediate the effects of CR on aging and longevity. These results also point to molecular mediators that can translate changes in substrate utilization to regulation of growth factor signaling as potential regulatory nodes necessary for the CR-induced effects on cell proliferation and longevity.
KISTI Vehicle-Based Urban Sensing Dataset

Recent smart city projects are rapidly expanding their technical boundary up to the Internet of Things, Big Data, and AI technology to realize the data-oriented urban situation awareness and making lots of sophisticated decision makings for various social issues such as traffic, air quality, boosting local economy, etc. In terms of cyber-physical systems, sensing data would be the first step to enable whole urban ecology to live and derive numerous benefits. In this work, in order to satisfy growing demands on urban environments by researchers and government officials, we have developed a vehicle-based urban sensing system to construct the basis to create urban sensing datasets. In this paper, we introduce our effort to collect urban sensing data collected in Daejeon City, Korea with two exclusive vehicles for three months (Sept.-Nov., 2019). We also describe what we can obtain from the collected datasets in terms of public benefits for establishing data-oriented smart cities.
Fault-Tolerance of the Nine-Phase Induction Machine under the Open-Switch Fault Condition

Inverter fed induction motors play a vital role in providing reliable operation in many industrial applications. However, the probability of failure of inverter switches is higher when compared to electro-mechanical systems. As the power electronic converter is the crucial unit in the drive, the failure of a semiconductor switch might lead to the failure of the entire drive. Therefore, it is an important concern to improve the fault-tolerance of the inverters to effectively utilize the induction motor drives (IMD). This paper proposes a reconfiguration of the nine-phase IMD to operate the system under the single-leg open-switch fault condition. ANSYS Maxwell is used for the FEM analysis of the nine-phase IMD under both normal as well as fault conditions.